Overview

Brought to you by YData

Dataset statistics

Number of variables51
Number of observations570464
Missing cells86027
Missing cells (%)0.3%
Duplicate rows2067
Duplicate rows (%)0.4%
Total size in memory222.0 MiB
Average record size in memory408.0 B

Variable types

Categorical26
Text10
Numeric15

Alerts

Dataset has 2067 (0.4%) duplicate rowsDuplicates
cole_area_ubicacion is highly overall correlated with cole_cod_dane_establecimiento and 1 other fieldsHigh correlation
cole_calendario is highly overall correlated with periodoHigh correlation
cole_cod_dane_establecimiento is highly overall correlated with cole_area_ubicacion and 2 other fieldsHigh correlation
cole_cod_dane_sede is highly overall correlated with cole_area_ubicacion and 2 other fieldsHigh correlation
cole_cod_depto_ubicacion is highly overall correlated with cole_cod_mcpio_ubicacion and 7 other fieldsHigh correlation
cole_cod_mcpio_ubicacion is highly overall correlated with cole_cod_depto_ubicacion and 7 other fieldsHigh correlation
cole_depto_ubicacion is highly overall correlated with cole_cod_depto_ubicacion and 6 other fieldsHigh correlation
cole_naturaleza is highly overall correlated with cole_cod_dane_establecimiento and 1 other fieldsHigh correlation
cole_sede_principal is highly overall correlated with estu_cod_reside_depto and 6 other fieldsHigh correlation
desemp_ingles is highly overall correlated with estu_estudiante and 3 other fieldsHigh correlation
estu_cod_depto_presentacion is highly overall correlated with cole_cod_depto_ubicacion and 8 other fieldsHigh correlation
estu_cod_mcpio_presentacion is highly overall correlated with cole_cod_depto_ubicacion and 7 other fieldsHigh correlation
estu_cod_reside_depto is highly overall correlated with cole_cod_depto_ubicacion and 10 other fieldsHigh correlation
estu_cod_reside_mcpio is highly overall correlated with cole_cod_depto_ubicacion and 7 other fieldsHigh correlation
estu_depto_presentacion is highly overall correlated with cole_cod_depto_ubicacion and 11 other fieldsHigh correlation
estu_depto_reside is highly overall correlated with cole_cod_depto_ubicacion and 7 other fieldsHigh correlation
estu_estadoinvestigacion is highly overall correlated with cole_sede_principal and 5 other fieldsHigh correlation
estu_estudiante is highly overall correlated with cole_sede_principal and 16 other fieldsHigh correlation
estu_privado_libertad is highly overall correlated with cole_sede_principal and 11 other fieldsHigh correlation
fami_cuartoshogar is highly overall correlated with estu_estudiante and 1 other fieldsHigh correlation
fami_educacionmadre is highly overall correlated with cole_sede_principal and 3 other fieldsHigh correlation
fami_educacionpadre is highly overall correlated with estu_estudiante and 1 other fieldsHigh correlation
fami_estratovivienda is highly overall correlated with estu_cod_reside_depto and 3 other fieldsHigh correlation
fami_personashogar is highly overall correlated with cole_sede_principal and 4 other fieldsHigh correlation
fami_tieneautomovil is highly overall correlated with estu_estudiante and 2 other fieldsHigh correlation
fami_tienecomputador is highly overall correlated with estu_estudiante and 3 other fieldsHigh correlation
fami_tieneinternet is highly overall correlated with fami_estratovivienda and 1 other fieldsHigh correlation
periodo is highly overall correlated with cole_calendarioHigh correlation
punt_c_naturales is highly overall correlated with punt_global and 4 other fieldsHigh correlation
punt_global is highly overall correlated with desemp_ingles and 7 other fieldsHigh correlation
punt_ingles is highly overall correlated with desemp_ingles and 6 other fieldsHigh correlation
punt_lectura_critica is highly overall correlated with punt_c_naturales and 4 other fieldsHigh correlation
punt_matematicas is highly overall correlated with punt_c_naturales and 4 other fieldsHigh correlation
punt_sociales_ciudadanas is highly overall correlated with punt_c_naturales and 4 other fieldsHigh correlation
periodo is highly imbalanced (73.3%) Imbalance
estu_tipodocumento is highly imbalanced (74.6%) Imbalance
cole_bilingue is highly imbalanced (86.4%) Imbalance
cole_calendario is highly imbalanced (85.2%) Imbalance
cole_genero is highly imbalanced (83.3%) Imbalance
cole_sede_principal is highly imbalanced (95.3%) Imbalance
estu_estadoinvestigacion is highly imbalanced (99.2%) Imbalance
estu_estudiante is highly imbalanced (99.5%) Imbalance
estu_privado_libertad is highly imbalanced (99.4%) Imbalance
fami_estratovivienda is highly imbalanced (55.6%) Imbalance
fami_tieneautomovil is highly imbalanced (81.0%) Imbalance
fami_tienecomputador is highly imbalanced (65.8%) Imbalance
cole_bilingue has 69727 (12.2%) missing values Missing
estu_cod_reside_depto is highly skewed (γ1 = 40.90316454) Skewed

Reproduction

Analysis started2025-05-25 17:55:04.232952
Analysis finished2025-05-25 17:56:26.798598
Duration1 minute and 22.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

periodo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
20152
544491 
20151
 
25973

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2852320
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20152
2nd row20152
3rd row20152
4th row20152
5th row20152

Common Values

ValueCountFrequency (%)
20152 544491
95.4%
20151 25973
 
4.6%

Length

2025-05-25T12:56:26.834550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:26.874465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
20152 544491
95.4%
20151 25973
 
4.6%

Most occurring characters

ValueCountFrequency (%)
2 1114955
39.1%
1 596437
20.9%
0 570464
20.0%
5 570464
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2852320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1114955
39.1%
1 596437
20.9%
0 570464
20.0%
5 570464
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2852320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1114955
39.1%
1 596437
20.9%
0 570464
20.0%
5 570464
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2852320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1114955
39.1%
1 596437
20.9%
0 570464
20.0%
5 570464
20.0%

estu_tipodocumento
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
TI
449535 
CC
110041 
CR
 
10466
CE
 
307
PE
 
84
Other values (5)
 
31

Length

Max length3
Median length2
Mean length1.9999947
Min length1

Characters and Unicode

Total characters1140925
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTI
2nd rowTI
3rd rowTI
4th rowTI
5th rowTI

Common Values

ValueCountFrequency (%)
TI 449535
78.8%
CC 110041
 
19.3%
CR 10466
 
1.8%
CE 307
 
0.1%
PE 84
 
< 0.1%
PC 12
 
< 0.1%
RC 10
 
< 0.1%
V 6
 
< 0.1%
NIP 2
 
< 0.1%
NUI 1
 
< 0.1%

Length

2025-05-25T12:56:26.920071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:26.968218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ti 449535
78.8%
cc 110041
 
19.3%
cr 10466
 
1.8%
ce 307
 
0.1%
pe 84
 
< 0.1%
pc 12
 
< 0.1%
rc 10
 
< 0.1%
v 6
 
< 0.1%
nip 2
 
< 0.1%
nui 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 449538
39.4%
T 449535
39.4%
C 230877
20.2%
R 10476
 
0.9%
E 391
 
< 0.1%
P 98
 
< 0.1%
V 6
 
< 0.1%
N 3
 
< 0.1%
U 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1140925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 449538
39.4%
T 449535
39.4%
C 230877
20.2%
R 10476
 
0.9%
E 391
 
< 0.1%
P 98
 
< 0.1%
V 6
 
< 0.1%
N 3
 
< 0.1%
U 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1140925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 449538
39.4%
T 449535
39.4%
C 230877
20.2%
R 10476
 
0.9%
E 391
 
< 0.1%
P 98
 
< 0.1%
V 6
 
< 0.1%
N 3
 
< 0.1%
U 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1140925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 449538
39.4%
T 449535
39.4%
C 230877
20.2%
R 10476
 
0.9%
E 391
 
< 0.1%
P 98
 
< 0.1%
V 6
 
< 0.1%
N 3
 
< 0.1%
U 1
 
< 0.1%
Distinct568397
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:27.184385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters9127424
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique566330 ?
Unique (%)99.3%

Sample

1st rowSB11201520043269
2nd rowSB11201520486210
3rd rowSB11201520344372
4th rowSB11201520036842
5th rowSB11201520335295
ValueCountFrequency (%)
sb11201520057011 2
 
< 0.1%
sb11201520021180 2
 
< 0.1%
sb11201520136601 2
 
< 0.1%
sb11201520105136 2
 
< 0.1%
sb11201520132317 2
 
< 0.1%
sb11201520537840 2
 
< 0.1%
sb11201520430195 2
 
< 0.1%
sb11201520264144 2
 
< 0.1%
sb11201520538792 2
 
< 0.1%
sb11201520033977 2
 
< 0.1%
Other values (568387) 570444
> 99.9%
2025-05-25T12:56:27.408868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2124897
23.3%
0 1549605
17.0%
2 1497977
16.4%
5 926017
10.1%
S 570464
 
6.2%
B 570464
 
6.2%
3 381344
 
4.2%
4 379040
 
4.2%
6 286147
 
3.1%
7 282614
 
3.1%
Other values (2) 558855
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9127424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2124897
23.3%
0 1549605
17.0%
2 1497977
16.4%
5 926017
10.1%
S 570464
 
6.2%
B 570464
 
6.2%
3 381344
 
4.2%
4 379040
 
4.2%
6 286147
 
3.1%
7 282614
 
3.1%
Other values (2) 558855
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9127424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2124897
23.3%
0 1549605
17.0%
2 1497977
16.4%
5 926017
10.1%
S 570464
 
6.2%
B 570464
 
6.2%
3 381344
 
4.2%
4 379040
 
4.2%
6 286147
 
3.1%
7 282614
 
3.1%
Other values (2) 558855
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9127424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2124897
23.3%
0 1549605
17.0%
2 1497977
16.4%
5 926017
10.1%
S 570464
 
6.2%
B 570464
 
6.2%
3 381344
 
4.2%
4 379040
 
4.2%
6 286147
 
3.1%
7 282614
 
3.1%
Other values (2) 558855
 
6.1%

cole_area_ubicacion
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
URBANO
490822 
RURAL
79642 

Length

Max length6
Median length6
Mean length5.8603908
Min length5

Characters and Unicode

Total characters3343142
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANO
2nd rowURBANO
3rd rowRURAL
4th rowURBANO
5th rowRURAL

Common Values

ValueCountFrequency (%)
URBANO 490822
86.0%
RURAL 79642
 
14.0%

Length

2025-05-25T12:56:27.461138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:27.497508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
urbano 490822
86.0%
rural 79642
 
14.0%

Most occurring characters

ValueCountFrequency (%)
R 650106
19.4%
U 570464
17.1%
A 570464
17.1%
B 490822
14.7%
N 490822
14.7%
O 490822
14.7%
L 79642
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3343142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 650106
19.4%
U 570464
17.1%
A 570464
17.1%
B 490822
14.7%
N 490822
14.7%
O 490822
14.7%
L 79642
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3343142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 650106
19.4%
U 570464
17.1%
A 570464
17.1%
B 490822
14.7%
N 490822
14.7%
O 490822
14.7%
L 79642
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3343142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 650106
19.4%
U 570464
17.1%
A 570464
17.1%
B 490822
14.7%
N 490822
14.7%
O 490822
14.7%
L 79642
 
2.4%

cole_bilingue
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing69727
Missing (%)12.2%
Memory size4.4 MiB
N
491209 
S
 
9528

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500737
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowS
5th rowN

Common Values

ValueCountFrequency (%)
N 491209
86.1%
S 9528
 
1.7%
(Missing) 69727
 
12.2%

Length

2025-05-25T12:56:27.536936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:27.571486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
n 491209
98.1%
s 9528
 
1.9%

Most occurring characters

ValueCountFrequency (%)
N 491209
98.1%
S 9528
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 491209
98.1%
S 9528
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 491209
98.1%
S 9528
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 491209
98.1%
S 9528
 
1.9%

cole_calendario
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
A
551431 
B
 
15192
OTRO
 
3841

Length

Max length4
Median length1
Mean length1.0201993
Min length1

Characters and Unicode

Total characters581987
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 551431
96.7%
B 15192
 
2.7%
OTRO 3841
 
0.7%

Length

2025-05-25T12:56:27.611787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:27.650109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
a 551431
96.7%
b 15192
 
2.7%
otro 3841
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 551431
94.7%
B 15192
 
2.6%
O 7682
 
1.3%
T 3841
 
0.7%
R 3841
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 551431
94.7%
B 15192
 
2.6%
O 7682
 
1.3%
T 3841
 
0.7%
R 3841
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 551431
94.7%
B 15192
 
2.6%
O 7682
 
1.3%
T 3841
 
0.7%
R 3841
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 551431
94.7%
B 15192
 
2.6%
O 7682
 
1.3%
T 3841
 
0.7%
R 3841
 
0.7%

cole_caracter
Categorical

Distinct4
Distinct (%)< 0.1%
Missing3703
Missing (%)0.6%
Memory size4.4 MiB
ACADÉMICO
313766 
TÉCNICO/ACADÉMICO
190766 
TÉCNICO
60349 
NO APLICA
 
1880

Length

Max length19
Median length10
Mean length12.813031
Min length8

Characters and Unicode

Total characters7261926
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACADÉMICO
2nd rowTÉCNICO/ACADÉMICO
3rd rowACADÉMICO
4th rowACADÉMICO
5th rowACADÉMICO

Common Values

ValueCountFrequency (%)
ACADÉMICO 313766
55.0%
TÉCNICO/ACADÉMICO 190766
33.4%
TÉCNICO 60349
 
10.6%
NO APLICA 1880
 
0.3%
(Missing) 3703
 
0.6%

Length

2025-05-25T12:56:27.692953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:27.734579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
acadã‰mico 313766
55.2%
tã‰cnico/acadã‰mico 190766
33.5%
tã‰cnico 60349
 
10.6%
no 1880
 
0.3%
aplica 1880
 
0.3%

Most occurring characters

ValueCountFrequency (%)
C 1513174
20.8%
A 1012824
13.9%
I 757527
10.4%
O 757527
10.4%
à 755647
10.4%
‰ 755647
10.4%
D 504532
 
6.9%
M 504532
 
6.9%
N 252995
 
3.5%
T 251115
 
3.5%
Other values (4) 196406
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7261926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1513174
20.8%
A 1012824
13.9%
I 757527
10.4%
O 757527
10.4%
à 755647
10.4%
‰ 755647
10.4%
D 504532
 
6.9%
M 504532
 
6.9%
N 252995
 
3.5%
T 251115
 
3.5%
Other values (4) 196406
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7261926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1513174
20.8%
A 1012824
13.9%
I 757527
10.4%
O 757527
10.4%
à 755647
10.4%
‰ 755647
10.4%
D 504532
 
6.9%
M 504532
 
6.9%
N 252995
 
3.5%
T 251115
 
3.5%
Other values (4) 196406
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7261926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1513174
20.8%
A 1012824
13.9%
I 757527
10.4%
O 757527
10.4%
à 755647
10.4%
‰ 755647
10.4%
D 504532
 
6.9%
M 504532
 
6.9%
N 252995
 
3.5%
T 251115
 
3.5%
Other values (4) 196406
 
2.7%

cole_cod_dane_establecimiento
Real number (ℝ)

High correlation 

Distinct10046
Distinct (%)1.8%
Missing67
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.1047974 × 1011
Minimum1.05001 × 1011
Maximum5.68432 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:27.783669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05266 × 1011
Q11.19001 × 1011
median1.7600101 × 1011
Q33.11001 × 1011
95-th percentile3.76001 × 1011
Maximum5.68432 × 1011
Range4.63431 × 1011
Interquartile range (IQR)1.92 × 1011

Descriptive statistics

Standard deviation9.5373205 × 1010
Coefficient of variation (CV)0.45312296
Kurtosis-1.006629
Mean2.1047974 × 1011
Median Absolute Deviation (MAD)6.4999992 × 1010
Skewness0.54291174
Sum1.2005701 × 1017
Variance9.0960482 × 1021
MonotonicityNot monotonic
2025-05-25T12:56:27.837894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.110011059 × 10111119
 
0.2%
1.050010001 × 1011987
 
0.2%
3.050010079 × 1011765
 
0.1%
1.050010133 × 1011686
 
0.1%
3.110011054 × 1011675
 
0.1%
1.056150002 × 1011618
 
0.1%
4.050010169 × 1011595
 
0.1%
3.050010174 × 1011592
 
0.1%
1.110010247 × 1011587
 
0.1%
1.760010058 × 1011581
 
0.1%
Other values (10036) 563192
98.7%
ValueCountFrequency (%)
1.05001 × 101124
 
< 0.1%
1.05001 × 101199
 
< 0.1%
1.050010001 × 1011987
0.2%
1.050010001 × 101179
 
< 0.1%
1.050010001 × 101125
 
< 0.1%
1.050010002 × 101131
 
< 0.1%
1.050010002 × 101175
 
< 0.1%
1.050010002 × 101136
 
< 0.1%
1.050010003 × 1011172
 
< 0.1%
1.050010004 × 101150
 
< 0.1%
ValueCountFrequency (%)
5.684320013 × 10117
 
< 0.1%
5.680010097 × 10117
 
< 0.1%
5.25843 × 101138
< 0.1%
5.19142 × 101124
 
< 0.1%
5.19001 × 101194
< 0.1%
4.99760001 × 10117
 
< 0.1%
4.990010019 × 101138
< 0.1%
4.990010012 × 101116
 
< 0.1%
4.950010037 × 101135
 
< 0.1%
4.86865001 × 101112
 
< 0.1%

cole_cod_dane_sede
Real number (ℝ)

High correlation 

Distinct10290
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1172519 × 1011
Minimum1.05001 × 1011
Maximum8.54874 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:27.889650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05266 × 1011
Q11.19001 × 1011
median1.7600101 × 1011
Q33.11001 × 1011
95-th percentile3.7600101 × 1011
Maximum8.54874 × 1011
Range7.49873 × 1011
Interquartile range (IQR)1.92 × 1011

Descriptive statistics

Standard deviation9.867031 × 1010
Coefficient of variation (CV)0.4660301
Kurtosis0.89127683
Mean2.1172519 × 1011
Median Absolute Deviation (MAD)6.4999995 × 1010
Skewness0.8320938
Sum1.207816 × 1017
Variance9.7358301 × 1021
MonotonicityNot monotonic
2025-05-25T12:56:27.948334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.110011059 × 10111119
 
0.2%
1.050010001 × 1011987
 
0.2%
3.050010079 × 1011765
 
0.1%
1.050010133 × 1011686
 
0.1%
3.110011054 × 1011675
 
0.1%
1.056150002 × 1011618
 
0.1%
4.050010169 × 1011595
 
0.1%
3.050010174 × 1011592
 
0.1%
1.110010247 × 1011587
 
0.1%
1.760010058 × 1011581
 
0.1%
Other values (10280) 563259
98.7%
ValueCountFrequency (%)
1.05001 × 101124
 
< 0.1%
1.05001 × 101199
 
< 0.1%
1.050010001 × 1011987
0.2%
1.050010001 × 101179
 
< 0.1%
1.050010001 × 101125
 
< 0.1%
1.050010002 × 101131
 
< 0.1%
1.050010002 × 101175
 
< 0.1%
1.050010002 × 101136
 
< 0.1%
1.050010003 × 1011172
 
< 0.1%
1.050010004 × 101150
 
< 0.1%
ValueCountFrequency (%)
8.54874 × 10118
 
< 0.1%
8.47001 × 101121
 
< 0.1%
8.47001 × 101174
 
< 0.1%
8.47001 × 101154
 
< 0.1%
8.47001 × 1011193
< 0.1%
8.180011 × 101119
 
< 0.1%
8.180011 × 101180
< 0.1%
8.180011 × 101172
 
< 0.1%
8.180011 × 101142
 
< 0.1%
8.1343 × 101190
< 0.1%

cole_cod_depto_ubicacion
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.948503
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:28.002264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.806804
Coefficient of variation (CV)0.81359702
Kurtosis-1.1906464
Mean32.948503
Median Absolute Deviation (MAD)15
Skewness0.6146687
Sum18795935
Variance718.60475
MonotonicityNot monotonic
2025-05-25T12:56:28.049484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 96838
17.0%
5 76173
13.4%
76 49476
 
8.7%
25 37629
 
6.6%
8 30378
 
5.3%
68 26967
 
4.7%
13 24928
 
4.4%
23 18991
 
3.3%
52 18033
 
3.2%
73 17403
 
3.1%
Other values (23) 173648
30.4%
ValueCountFrequency (%)
5 76173
13.4%
8 30378
 
5.3%
11 96838
17.0%
13 24928
 
4.4%
15 16837
 
3.0%
17 11279
 
2.0%
18 4063
 
0.7%
19 15449
 
2.7%
20 12213
 
2.1%
23 18991
 
3.3%
ValueCountFrequency (%)
99 469
 
0.1%
97 379
 
0.1%
95 935
 
0.2%
94 209
 
< 0.1%
91 827
 
0.1%
88 701
 
0.1%
86 4358
 
0.8%
85 5884
 
1.0%
81 3017
 
0.5%
76 49476
8.7%

cole_cod_mcpio_ubicacion
Real number (ℝ)

High correlation 

Distinct1109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33174.117
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:28.098360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20238
Q354498
95-th percentile76364
Maximum99773
Range94772
Interquartile range (IQR)43497

Descriptive statistics

Standard deviation26830.541
Coefficient of variation (CV)0.80877935
Kurtosis-1.193289
Mean33174.117
Median Absolute Deviation (MAD)14631
Skewness0.61130231
Sum1.892464 × 1010
Variance7.1987793 × 108
MonotonicityNot monotonic
2025-05-25T12:56:28.150599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 96838
 
17.0%
5001 31127
 
5.5%
76001 26028
 
4.6%
8001 17104
 
3.0%
13001 13145
 
2.3%
54001 8944
 
1.6%
73001 7774
 
1.4%
68001 7028
 
1.2%
50001 6740
 
1.2%
25754 6701
 
1.2%
Other values (1099) 349035
61.2%
ValueCountFrequency (%)
5001 31127
5.5%
5002 169
 
< 0.1%
5004 29
 
< 0.1%
5021 43
 
< 0.1%
5030 307
 
0.1%
5031 300
 
0.1%
5034 438
 
0.1%
5036 49
 
< 0.1%
5038 103
 
< 0.1%
5040 157
 
< 0.1%
ValueCountFrequency (%)
99773 113
 
< 0.1%
99624 41
 
< 0.1%
99524 109
 
< 0.1%
99001 206
 
< 0.1%
97889 13
 
< 0.1%
97001 366
0.1%
95200 28
 
< 0.1%
95025 136
 
< 0.1%
95015 55
 
< 0.1%
95001 716
0.1%

cole_codigo_icfes
Real number (ℝ)

Distinct12482
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80665.562
Minimum18
Maximum631622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:28.200603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile5082
Q129215
median80176
Q3124750
95-th percentile169920
Maximum631622
Range631604
Interquartile range (IQR)95535

Descriptive statistics

Standard deviation54457.391
Coefficient of variation (CV)0.67510087
Kurtosis-0.50155444
Mean80665.562
Median Absolute Deviation (MAD)47627
Skewness0.24976406
Sum4.6016799 × 1010
Variance2.9656075 × 109
MonotonicityNot monotonic
2025-05-25T12:56:28.320483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 987
 
0.2%
786 686
 
0.1%
79848 675
 
0.1%
164491 637
 
0.1%
88617 570
 
0.1%
156786 549
 
0.1%
83931 523
 
0.1%
164590 490
 
0.1%
145383 469
 
0.1%
1073 424
 
0.1%
Other values (12472) 564454
98.9%
ValueCountFrequency (%)
18 5
 
< 0.1%
59 987
0.2%
75 47
 
< 0.1%
83 30
 
< 0.1%
91 77
 
< 0.1%
125 69
 
< 0.1%
141 113
 
< 0.1%
174 105
 
< 0.1%
182 109
 
< 0.1%
190 90
 
< 0.1%
ValueCountFrequency (%)
631622 37
< 0.1%
197624 10
 
< 0.1%
197525 11
 
< 0.1%
197517 10
 
< 0.1%
197467 4
 
< 0.1%
197459 54
< 0.1%
197434 8
 
< 0.1%
197400 9
 
< 0.1%
197392 18
 
< 0.1%
197384 16
 
< 0.1%

cole_depto_ubicacion
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
BOGOTA
96838 
ANTIOQUIA
76173 
VALLE
49476 
CUNDINAMARCA
37629 
ATLANTICO
30378 
Other values (28)
279970 

Length

Max length15
Median length12
Mean length7.4804107
Min length4

Characters and Unicode

Total characters4267305
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANTIOQUIA
2nd rowBOGOTA
3rd rowHUILA
4th rowBOGOTA
5th rowBOLIVAR

Common Values

ValueCountFrequency (%)
BOGOTA 96838
17.0%
ANTIOQUIA 76173
13.4%
VALLE 49476
 
8.7%
CUNDINAMARCA 37629
 
6.6%
ATLANTICO 30378
 
5.3%
SANTANDER 26967
 
4.7%
BOLIVAR 24928
 
4.4%
CORDOBA 18991
 
3.3%
NARIÑO 18033
 
3.2%
TOLIMA 17403
 
3.1%
Other values (23) 173648
30.4%

Length

2025-05-25T12:56:28.373181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogota 96838
16.3%
antioquia 76173
12.8%
valle 49476
 
8.3%
santander 42681
 
7.2%
cundinamarca 37629
 
6.3%
atlantico 30378
 
5.1%
bolivar 24928
 
4.2%
cordoba 18991
 
3.2%
nariã‘o 18033
 
3.0%
tolima 17403
 
2.9%
Other values (25) 181567
30.6%

Most occurring characters

ValueCountFrequency (%)
A 907975
21.3%
O 451538
10.6%
N 331477
 
7.8%
T 330000
 
7.7%
I 328374
 
7.7%
L 229709
 
5.4%
C 228781
 
5.4%
R 220756
 
5.2%
U 185947
 
4.4%
E 170562
 
4.0%
Other values (15) 882186
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4267305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 907975
21.3%
O 451538
10.6%
N 331477
 
7.8%
T 330000
 
7.7%
I 328374
 
7.7%
L 229709
 
5.4%
C 228781
 
5.4%
R 220756
 
5.2%
U 185947
 
4.4%
E 170562
 
4.0%
Other values (15) 882186
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4267305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 907975
21.3%
O 451538
10.6%
N 331477
 
7.8%
T 330000
 
7.7%
I 328374
 
7.7%
L 229709
 
5.4%
C 228781
 
5.4%
R 220756
 
5.2%
U 185947
 
4.4%
E 170562
 
4.0%
Other values (15) 882186
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4267305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 907975
21.3%
O 451538
10.6%
N 331477
 
7.8%
T 330000
 
7.7%
I 328374
 
7.7%
L 229709
 
5.4%
C 228781
 
5.4%
R 220756
 
5.2%
U 185947
 
4.4%
E 170562
 
4.0%
Other values (15) 882186
20.7%

cole_genero
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
MIXTO
548502 
FEMENINO
 
16938
MASCULINO
 
5024

Length

Max length9
Median length5
Mean length5.1243023
Min length5

Characters and Unicode

Total characters2923230
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIXTO
2nd rowMIXTO
3rd rowMIXTO
4th rowMIXTO
5th rowMIXTO

Common Values

ValueCountFrequency (%)
MIXTO 548502
96.2%
FEMENINO 16938
 
3.0%
MASCULINO 5024
 
0.9%

Length

2025-05-25T12:56:28.423118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:28.463521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
mixto 548502
96.2%
femenino 16938
 
3.0%
masculino 5024
 
0.9%

Most occurring characters

ValueCountFrequency (%)
M 570464
19.5%
I 570464
19.5%
O 570464
19.5%
X 548502
18.8%
T 548502
18.8%
N 38900
 
1.3%
E 33876
 
1.2%
F 16938
 
0.6%
A 5024
 
0.2%
S 5024
 
0.2%
Other values (3) 15072
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2923230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 570464
19.5%
I 570464
19.5%
O 570464
19.5%
X 548502
18.8%
T 548502
18.8%
N 38900
 
1.3%
E 33876
 
1.2%
F 16938
 
0.6%
A 5024
 
0.2%
S 5024
 
0.2%
Other values (3) 15072
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2923230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 570464
19.5%
I 570464
19.5%
O 570464
19.5%
X 548502
18.8%
T 548502
18.8%
N 38900
 
1.3%
E 33876
 
1.2%
F 16938
 
0.6%
A 5024
 
0.2%
S 5024
 
0.2%
Other values (3) 15072
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2923230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 570464
19.5%
I 570464
19.5%
O 570464
19.5%
X 548502
18.8%
T 548502
18.8%
N 38900
 
1.3%
E 33876
 
1.2%
F 16938
 
0.6%
A 5024
 
0.2%
S 5024
 
0.2%
Other values (3) 15072
 
0.5%

cole_jornada
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
MAÑANA
290675 
COMPLETA
117098 
TARDE
83517 
NOCHE
39520 
SABATINA
39429 

Length

Max length8
Median length7
Mean length6.8422389
Min length5

Characters and Unicode

Total characters3903251
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTARDE
2nd rowMAÑANA
3rd rowCOMPLETA
4th rowCOMPLETA
5th rowMAÑANA

Common Values

ValueCountFrequency (%)
MAÑANA 290675
51.0%
COMPLETA 117098
20.5%
TARDE 83517
 
14.6%
NOCHE 39520
 
6.9%
SABATINA 39429
 
6.9%
UNICA 225
 
< 0.1%

Length

2025-05-25T12:56:28.505102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:28.547246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
maã‘ana 290675
51.0%
completa 117098
20.5%
tarde 83517
 
14.6%
noche 39520
 
6.9%
sabatina 39429
 
6.9%
unica 225
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 1191152
30.5%
M 407773
 
10.4%
N 369849
 
9.5%
à 290675
 
7.4%
‘ 290675
 
7.4%
E 240135
 
6.2%
T 240044
 
6.1%
C 156843
 
4.0%
O 156618
 
4.0%
P 117098
 
3.0%
Other values (8) 442389
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3903251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1191152
30.5%
M 407773
 
10.4%
N 369849
 
9.5%
à 290675
 
7.4%
‘ 290675
 
7.4%
E 240135
 
6.2%
T 240044
 
6.1%
C 156843
 
4.0%
O 156618
 
4.0%
P 117098
 
3.0%
Other values (8) 442389
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3903251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1191152
30.5%
M 407773
 
10.4%
N 369849
 
9.5%
à 290675
 
7.4%
‘ 290675
 
7.4%
E 240135
 
6.2%
T 240044
 
6.1%
C 156843
 
4.0%
O 156618
 
4.0%
P 117098
 
3.0%
Other values (8) 442389
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3903251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1191152
30.5%
M 407773
 
10.4%
N 369849
 
9.5%
à 290675
 
7.4%
‘ 290675
 
7.4%
E 240135
 
6.2%
T 240044
 
6.1%
C 156843
 
4.0%
O 156618
 
4.0%
P 117098
 
3.0%
Other values (8) 442389
 
11.3%
Distinct1026
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:28.683918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length24
Mean length9.0072187
Min length3

Characters and Unicode

Total characters5138294
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA CEJA
2nd rowBOGOTÁ D.C.
3rd rowIQUIRA
4th rowBOGOTÁ D.C.
5th rowMAGANGUE
ValueCountFrequency (%)
bogotだ96838
 
12.5%
d.c 96838
 
12.5%
medellin 31127
 
4.0%
cali 26028
 
3.4%
san 17660
 
2.3%
barranquilla 17104
 
2.2%
cartagena 13364
 
1.7%
de 11879
 
1.5%
la 11410
 
1.5%
santa 9502
 
1.2%
Other values (1033) 440127
57.0%
2025-05-25T12:56:28.878184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 743318
14.5%
O 431217
 
8.4%
E 315784
 
6.1%
I 312178
 
6.1%
L 305346
 
5.9%
C 304681
 
5.9%
N 259518
 
5.1%
R 257618
 
5.0%
T 251359
 
4.9%
D 216668
 
4.2%
Other values (24) 1740607
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5138294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 743318
14.5%
O 431217
 
8.4%
E 315784
 
6.1%
I 312178
 
6.1%
L 305346
 
5.9%
C 304681
 
5.9%
N 259518
 
5.1%
R 257618
 
5.0%
T 251359
 
4.9%
D 216668
 
4.2%
Other values (24) 1740607
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5138294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 743318
14.5%
O 431217
 
8.4%
E 315784
 
6.1%
I 312178
 
6.1%
L 305346
 
5.9%
C 304681
 
5.9%
N 259518
 
5.1%
R 257618
 
5.0%
T 251359
 
4.9%
D 216668
 
4.2%
Other values (24) 1740607
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5138294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 743318
14.5%
O 431217
 
8.4%
E 315784
 
6.1%
I 312178
 
6.1%
L 305346
 
5.9%
C 304681
 
5.9%
N 259518
 
5.1%
R 257618
 
5.0%
T 251359
 
4.9%
D 216668
 
4.2%
Other values (24) 1740607
33.9%

cole_naturaleza
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
OFICIAL
407690 
NO OFICIAL
162774 

Length

Max length10
Median length7
Mean length7.8560084
Min length7

Characters and Unicode

Total characters4481570
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFICIAL
2nd rowOFICIAL
3rd rowOFICIAL
4th rowNO OFICIAL
5th rowOFICIAL

Common Values

ValueCountFrequency (%)
OFICIAL 407690
71.5%
NO OFICIAL 162774
 
28.5%

Length

2025-05-25T12:56:28.931554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:28.968948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
oficial 570464
77.8%
no 162774
 
22.2%

Most occurring characters

ValueCountFrequency (%)
I 1140928
25.5%
O 733238
16.4%
F 570464
12.7%
C 570464
12.7%
A 570464
12.7%
L 570464
12.7%
N 162774
 
3.6%
162774
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4481570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1140928
25.5%
O 733238
16.4%
F 570464
12.7%
C 570464
12.7%
A 570464
12.7%
L 570464
12.7%
N 162774
 
3.6%
162774
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4481570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1140928
25.5%
O 733238
16.4%
F 570464
12.7%
C 570464
12.7%
A 570464
12.7%
L 570464
12.7%
N 162774
 
3.6%
162774
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4481570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1140928
25.5%
O 733238
16.4%
F 570464
12.7%
C 570464
12.7%
A 570464
12.7%
L 570464
12.7%
N 162774
 
3.6%
162774
 
3.6%
Distinct9110
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:29.104949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length105
Median length79
Mean length34.661172
Min length4

Characters and Unicode

Total characters19772951
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st rowI. E. MONSEÑOR ALFONSO URIBE JARAMILLO
2nd rowCOLEGIO INEM SANTIAGO PEREZ (IED)
3rd rowINSTITUCION EDUCATIVA KUE DSI J
4th rowCOLEGIO SOLES DEL SABER
5th rowINST EDUC SAN SEBASTIAN DE MADRID
ValueCountFrequency (%)
educativa 226402
 
8.4%
institucion 197666
 
7.3%
de 134216
 
5.0%
colegio 106225
 
3.9%
ied 51722
 
1.9%
col 47056
 
1.7%
san 46362
 
1.7%
inst 40612
 
1.5%
la 36272
 
1.3%
jose 33408
 
1.2%
Other values (5487) 1777102
65.9%
2025-05-25T12:56:29.314327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2155274
10.9%
I 2115130
10.7%
A 2033046
10.3%
E 1635355
 
8.3%
O 1453876
 
7.4%
N 1318638
 
6.7%
T 1306007
 
6.6%
C 1225104
 
6.2%
S 925089
 
4.7%
R 873977
 
4.4%
Other values (55) 4731455
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19772951
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2155274
10.9%
I 2115130
10.7%
A 2033046
10.3%
E 1635355
 
8.3%
O 1453876
 
7.4%
N 1318638
 
6.7%
T 1306007
 
6.6%
C 1225104
 
6.2%
S 925089
 
4.7%
R 873977
 
4.4%
Other values (55) 4731455
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19772951
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2155274
10.9%
I 2115130
10.7%
A 2033046
10.3%
E 1635355
 
8.3%
O 1453876
 
7.4%
N 1318638
 
6.7%
T 1306007
 
6.6%
C 1225104
 
6.2%
S 925089
 
4.7%
R 873977
 
4.4%
Other values (55) 4731455
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19772951
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2155274
10.9%
I 2115130
10.7%
A 2033046
10.3%
E 1635355
 
8.3%
O 1453876
 
7.4%
N 1318638
 
6.7%
T 1306007
 
6.6%
C 1225104
 
6.2%
S 925089
 
4.7%
R 873977
 
4.4%
Other values (55) 4731455
23.9%
Distinct9625
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:29.429923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length101
Median length79
Mean length32.049991
Min length3

Characters and Unicode

Total characters18283366
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowI. E. MONSEÑOR ALFONSO URIBE JARAMILLO
2nd rowINEM SANTIAGO PEREZ
3rd rowKUE DSI J
4th rowCOL SOLES DEL SABER
5th rowSAN SEBASTIAN DE MADRID
ValueCountFrequency (%)
de 133462
 
4.7%
sede 110403
 
3.9%
102181
 
3.6%
col 94938
 
3.4%
principal 88931
 
3.2%
educativa 80208
 
2.8%
institucion 66488
 
2.4%
colegio 64636
 
2.3%
educ 63929
 
2.3%
inst 60760
 
2.2%
Other values (5903) 1955003
69.3%
2025-05-25T12:56:29.605503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2276917
12.5%
A 1769405
 
9.7%
E 1659125
 
9.1%
I 1649618
 
9.0%
O 1272020
 
7.0%
N 1131167
 
6.2%
C 1075410
 
5.9%
S 939509
 
5.1%
L 925020
 
5.1%
R 924819
 
5.1%
Other values (69) 4660356
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18283366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2276917
12.5%
A 1769405
 
9.7%
E 1659125
 
9.1%
I 1649618
 
9.0%
O 1272020
 
7.0%
N 1131167
 
6.2%
C 1075410
 
5.9%
S 939509
 
5.1%
L 925020
 
5.1%
R 924819
 
5.1%
Other values (69) 4660356
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18283366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2276917
12.5%
A 1769405
 
9.7%
E 1659125
 
9.1%
I 1649618
 
9.0%
O 1272020
 
7.0%
N 1131167
 
6.2%
C 1075410
 
5.9%
S 939509
 
5.1%
L 925020
 
5.1%
R 924819
 
5.1%
Other values (69) 4660356
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18283366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2276917
12.5%
A 1769405
 
9.7%
E 1659125
 
9.1%
I 1649618
 
9.0%
O 1272020
 
7.0%
N 1131167
 
6.2%
C 1075410
 
5.9%
S 939509
 
5.1%
L 925020
 
5.1%
R 924819
 
5.1%
Other values (69) 4660356
25.5%

cole_sede_principal
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
S
558286 
N
 
11776
SIETE DE AGOSTO"
 
120
COMUNA 17"
 
111
INDUSTRIAL Y TECNOLÓGICO ICIT"
 
66
Other values (5)
 
105

Length

Max length49
Median length1
Mean length1.0129701
Min length1

Characters and Unicode

Total characters577863
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 558286
97.9%
N 11776
 
2.1%
SIETE DE AGOSTO" 120
 
< 0.1%
COMUNA 17" 111
 
< 0.1%
INDUSTRIAL Y TECNOLÓGICO ICIT" 66
 
< 0.1%
EMPRESARIAL DE LOS ANDES" 41
 
< 0.1%
VALOR 23
 
< 0.1%
HNAS DE NSTRA SRA PAZ" 16
 
< 0.1%
INSTITUCION EDUCATIVA RAFAEL NU¿EZ DE SAN ANDRES 16
 
< 0.1%
TOCOTA" 9
 
< 0.1%

Length

2025-05-25T12:56:29.665639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:29.729549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
s 558286
97.7%
n 11776
 
2.1%
de 193
 
< 0.1%
siete 120
 
< 0.1%
agosto 120
 
< 0.1%
comuna 111
 
< 0.1%
17 111
 
< 0.1%
industrial 66
 
< 0.1%
y 66
 
< 0.1%
tecnolã“gico 66
 
< 0.1%
Other values (16) 381
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 558811
96.7%
N 12172
 
2.1%
1218
 
0.2%
E 686
 
0.1%
A 612
 
0.1%
O 581
 
0.1%
I 555
 
0.1%
T 520
 
0.1%
" 363
 
0.1%
C 350
 
0.1%
Other values (18) 1995
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 577863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 558811
96.7%
N 12172
 
2.1%
1218
 
0.2%
E 686
 
0.1%
A 612
 
0.1%
O 581
 
0.1%
I 555
 
0.1%
T 520
 
0.1%
" 363
 
0.1%
C 350
 
0.1%
Other values (18) 1995
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 577863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 558811
96.7%
N 12172
 
2.1%
1218
 
0.2%
E 686
 
0.1%
A 612
 
0.1%
O 581
 
0.1%
I 555
 
0.1%
T 520
 
0.1%
" 363
 
0.1%
C 350
 
0.1%
Other values (18) 1995
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 577863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 558811
96.7%
N 12172
 
2.1%
1218
 
0.2%
E 686
 
0.1%
A 612
 
0.1%
O 581
 
0.1%
I 555
 
0.1%
T 520
 
0.1%
" 363
 
0.1%
C 350
 
0.1%
Other values (18) 1995
 
0.3%

estu_cod_depto_presentacion
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing488
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean32.858961
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:29.785136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.837899
Coefficient of variation (CV)0.81676045
Kurtosis-1.1859606
Mean32.858961
Median Absolute Deviation (MAD)15
Skewness0.6201383
Sum18728819
Variance720.27285
MonotonicityNot monotonic
2025-05-25T12:56:29.832111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 98609
17.3%
5 76149
13.3%
76 49367
 
8.7%
25 35301
 
6.2%
8 31088
 
5.4%
68 26887
 
4.7%
13 24132
 
4.2%
23 18960
 
3.3%
52 18118
 
3.2%
15 17306
 
3.0%
Other values (23) 174059
30.5%
ValueCountFrequency (%)
5 76149
13.3%
8 31088
 
5.4%
11 98609
17.3%
13 24132
 
4.2%
15 17306
 
3.0%
17 11892
 
2.1%
18 4146
 
0.7%
19 14892
 
2.6%
20 12362
 
2.2%
23 18960
 
3.3%
ValueCountFrequency (%)
99 475
 
0.1%
97 378
 
0.1%
95 985
 
0.2%
94 208
 
< 0.1%
91 825
 
0.1%
88 708
 
0.1%
86 4360
 
0.8%
85 5965
 
1.0%
81 3179
 
0.6%
76 49367
8.7%

estu_cod_mcpio_presentacion
Real number (ℝ)

High correlation 

Distinct468
Distinct (%)0.1%
Missing109
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean33041.56
Minimum8
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:29.882941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile5001
Q111001
median20011
Q354498
95-th percentile76364
Maximum99773
Range99765
Interquartile range (IQR)43497

Descriptive statistics

Standard deviation26868.783
Coefficient of variation (CV)0.81318145
Kurtosis-1.1868981
Mean33041.56
Median Absolute Deviation (MAD)14470
Skewness0.61786312
Sum1.8845419 × 1010
Variance7.2193152 × 108
MonotonicityNot monotonic
2025-05-25T12:56:29.938176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 98609
 
17.3%
5001 29946
 
5.2%
76001 26008
 
4.6%
8001 18990
 
3.3%
13001 14333
 
2.5%
54001 9698
 
1.7%
73001 8750
 
1.5%
68001 8160
 
1.4%
50001 7100
 
1.2%
52001 7100
 
1.2%
Other values (458) 341661
59.9%
ValueCountFrequency (%)
8 1
 
< 0.1%
11 16
 
< 0.1%
13 5
 
< 0.1%
19 1
 
< 0.1%
41 41
 
< 0.1%
70 10
 
< 0.1%
76 304
 
0.1%
86 1
 
< 0.1%
5001 29946
5.2%
5002 199
 
< 0.1%
ValueCountFrequency (%)
99773 101
 
< 0.1%
99624 54
 
< 0.1%
99524 111
 
< 0.1%
99001 209
< 0.1%
97889 13
 
< 0.1%
97666 26
 
< 0.1%
97161 30
 
< 0.1%
97001 309
0.1%
95200 28
 
< 0.1%
95025 106
 
< 0.1%

estu_cod_reside_depto
Real number (ℝ)

High correlation  Skewed 

Distinct46
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean78.368624
Minimum5
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:29.992178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99999
Range99994
Interquartile range (IQR)43

Descriptive statistics

Standard deviation1821.5
Coefficient of variation (CV)23.24272
Kurtosis1687.6891
Mean78.368624
Median Absolute Deviation (MAD)15
Skewness40.903165
Sum44706087
Variance3317862.2
MonotonicityNot monotonic
2025-05-25T12:56:30.044942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
11 97959
17.2%
5 76221
13.4%
76 49403
 
8.7%
25 36355
 
6.4%
8 30314
 
5.3%
68 26631
 
4.7%
13 24865
 
4.4%
23 19026
 
3.3%
52 18037
 
3.2%
73 17502
 
3.1%
Other values (36) 174146
30.5%
ValueCountFrequency (%)
5 76221
13.4%
8 30314
 
5.3%
11 97959
17.2%
13 24865
 
4.4%
15 16964
 
3.0%
17 11455
 
2.0%
18 4049
 
0.7%
19 15235
 
2.7%
20 12210
 
2.1%
23 19026
 
3.3%
ValueCountFrequency (%)
99999 1
 
< 0.1%
86320 1
 
< 0.1%
76233 9
 
< 0.1%
76001 295
0.1%
70717 2
 
< 0.1%
70215 3
 
< 0.1%
70001 5
 
< 0.1%
41001 41
 
< 0.1%
19001 1
 
< 0.1%
13430 3
 
< 0.1%

estu_cod_reside_mcpio
Real number (ℝ)

High correlation 

Distinct1120
Distinct (%)0.2%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean33111.624
Minimum11
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:30.098253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile5001
Q111001
median20060
Q354498
95-th percentile76364
Maximum99999
Range99988
Interquartile range (IQR)43497

Descriptive statistics

Standard deviation26857.885
Coefficient of variation (CV)0.81113161
Kurtosis-1.1903959
Mean33111.624
Median Absolute Deviation (MAD)14481
Skewness0.6144028
Sum1.8888824 × 1010
Variance7.2134601 × 108
MonotonicityNot monotonic
2025-05-25T12:56:30.215916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 97959
 
17.2%
5001 30754
 
5.4%
76001 25594
 
4.5%
8001 17221
 
3.0%
13001 13193
 
2.3%
54001 8925
 
1.6%
73001 7842
 
1.4%
25754 6782
 
1.2%
68001 6745
 
1.2%
50001 6656
 
1.2%
Other values (1110) 348788
61.1%
ValueCountFrequency (%)
11 16
 
< 0.1%
13 16
 
< 0.1%
19 2
 
< 0.1%
41 41
 
< 0.1%
76 303
 
0.1%
86 1
 
< 0.1%
5001 30754
5.4%
5002 205
 
< 0.1%
5004 32
 
< 0.1%
5021 43
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
99773 114
 
< 0.1%
99624 42
 
< 0.1%
99524 110
 
< 0.1%
99001 207
< 0.1%
97889 13
 
< 0.1%
97666 26
 
< 0.1%
97161 30
 
< 0.1%
97001 310
0.1%
95200 29
 
< 0.1%

estu_depto_presentacion
Categorical

High correlation 

Distinct42
Distinct (%)< 0.1%
Missing86
Missing (%)< 0.1%
Memory size4.4 MiB
BOGOTÁ
98609 
ANTIOQUIA
76149 
VALLE
49367 
CUNDINAMARCA
35301 
ATLANTICO
31088 
Other values (37)
279864 

Length

Max length15
Median length12
Mean length7.6314216
Min length2

Characters and Unicode

Total characters4352795
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowANTIOQUIA
2nd rowBOGOTÁ
3rd rowHUILA
4th rowBOGOTÁ
5th rowBOLIVAR

Common Values

ValueCountFrequency (%)
BOGOTÁ 98609
17.3%
ANTIOQUIA 76149
13.3%
VALLE 49367
 
8.7%
CUNDINAMARCA 35301
 
6.2%
ATLANTICO 31088
 
5.4%
SANTANDER 26887
 
4.7%
BOLIVAR 24132
 
4.2%
CORDOBA 18960
 
3.3%
NARIÑO 18118
 
3.2%
BOYACA 17306
 
3.0%
Other values (32) 174461
30.6%

Length

2025-05-25T12:56:30.267796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotだ98609
16.6%
antioquia 76149
12.8%
valle 49367
 
8.3%
santander 42667
 
7.2%
cundinamarca 35301
 
5.9%
atlantico 31088
 
5.2%
bolivar 24132
 
4.1%
cordoba 18960
 
3.2%
nariã‘o 18118
 
3.0%
boyaca 17306
 
2.9%
Other values (34) 182361
30.7%

Most occurring characters

ValueCountFrequency (%)
A 805950
18.5%
O 454810
10.4%
T 332840
 
7.6%
N 327413
 
7.5%
I 325419
 
7.5%
L 229482
 
5.3%
C 225047
 
5.2%
R 218083
 
5.0%
U 183206
 
4.2%
E 170613
 
3.9%
Other values (26) 1079932
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4352795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 805950
18.5%
O 454810
10.4%
T 332840
 
7.6%
N 327413
 
7.5%
I 325419
 
7.5%
L 229482
 
5.3%
C 225047
 
5.2%
R 218083
 
5.0%
U 183206
 
4.2%
E 170613
 
3.9%
Other values (26) 1079932
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4352795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 805950
18.5%
O 454810
10.4%
T 332840
 
7.6%
N 327413
 
7.5%
I 325419
 
7.5%
L 229482
 
5.3%
C 225047
 
5.2%
R 218083
 
5.0%
U 183206
 
4.2%
E 170613
 
3.9%
Other values (26) 1079932
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4352795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 805950
18.5%
O 454810
10.4%
T 332840
 
7.6%
N 327413
 
7.5%
I 325419
 
7.5%
L 229482
 
5.3%
C 225047
 
5.2%
R 218083
 
5.0%
U 183206
 
4.2%
E 170613
 
3.9%
Other values (26) 1079932
24.8%

estu_depto_reside
Categorical

High correlation 

Distinct36
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size4.4 MiB
BOGOTÁ
97975 
ANTIOQUIA
76221 
VALLE
49707 
CUNDINAMARCA
36355 
ATLANTICO
30315 
Other values (31)
279886 

Length

Max length15
Median length12
Mean length7.6386454
Min length4

Characters and Unicode

Total characters4357534
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowANTIOQUIA
2nd rowBOGOTÁ
3rd rowHUILA
4th rowBOGOTÁ
5th rowBOLIVAR

Common Values

ValueCountFrequency (%)
BOGOTÁ 97975
17.2%
ANTIOQUIA 76221
13.4%
VALLE 49707
 
8.7%
CUNDINAMARCA 36355
 
6.4%
ATLANTICO 30315
 
5.3%
SANTANDER 26631
 
4.7%
BOLIVAR 24870
 
4.4%
CORDOBA 19026
 
3.3%
NARIÑO 18037
 
3.2%
TOLIMA 17502
 
3.1%
Other values (26) 173820
30.5%

Length

2025-05-25T12:56:30.316057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotだ97975
16.5%
antioquia 76221
12.8%
valle 49707
 
8.4%
santander 42373
 
7.1%
cundinamarca 36355
 
6.1%
atlantico 30315
 
5.1%
bolivar 24870
 
4.2%
cordoba 19026
 
3.2%
nariã‘o 18037
 
3.0%
tolima 17502
 
2.9%
Other values (28) 181811
30.6%

Most occurring characters

ValueCountFrequency (%)
A 807718
18.5%
O 453989
10.4%
T 330778
 
7.6%
N 328450
 
7.5%
I 327012
 
7.5%
L 230456
 
5.3%
C 226104
 
5.2%
R 219348
 
5.0%
U 184454
 
4.2%
E 170565
 
3.9%
Other values (21) 1078660
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4357534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 807718
18.5%
O 453989
10.4%
T 330778
 
7.6%
N 328450
 
7.5%
I 327012
 
7.5%
L 230456
 
5.3%
C 226104
 
5.2%
R 219348
 
5.0%
U 184454
 
4.2%
E 170565
 
3.9%
Other values (21) 1078660
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4357534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 807718
18.5%
O 453989
10.4%
T 330778
 
7.6%
N 328450
 
7.5%
I 327012
 
7.5%
L 230456
 
5.3%
C 226104
 
5.2%
R 219348
 
5.0%
U 184454
 
4.2%
E 170565
 
3.9%
Other values (21) 1078660
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4357534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 807718
18.5%
O 453989
10.4%
T 330778
 
7.6%
N 328450
 
7.5%
I 327012
 
7.5%
L 230456
 
5.3%
C 226104
 
5.2%
R 219348
 
5.0%
U 184454
 
4.2%
E 170565
 
3.9%
Other values (21) 1078660
24.8%

estu_estadoinvestigacion
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
PUBLICAR
569133 
VALIDEZ OFICINA JURÍDICA
 
920
VALLE
 
303
HUILA
 
41
CALDAS
 
23
Other values (5)
 
44

Length

Max length27
Median length8
Mean length8.0257597
Min length5

Characters and Unicode

Total characters4578407
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPUBLICAR
2nd rowPUBLICAR
3rd rowPUBLICAR
4th rowPUBLICAR
5th rowPUBLICAR

Common Values

ValueCountFrequency (%)
PUBLICAR 569133
99.8%
VALIDEZ OFICINA JURÍDICA 920
 
0.2%
VALLE 303
 
0.1%
HUILA 41
 
< 0.1%
CALDAS 23
 
< 0.1%
BOGOTÁ 16
 
< 0.1%
BOLIVAR 16
 
< 0.1%
PRESENTE CON LECTURA TARDIA 9
 
< 0.1%
CAUCA 2
 
< 0.1%
PUTUMAYO 1
 
< 0.1%

Length

2025-05-25T12:56:30.360846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:30.405296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
publicar 569133
99.4%
validez 920
 
0.2%
oficina 920
 
0.2%
jurãdica 920
 
0.2%
valle 303
 
0.1%
huila 41
 
< 0.1%
caldas 23
 
< 0.1%
bogotだ16
 
< 0.1%
bolivar 16
 
< 0.1%
presente 9
 
< 0.1%
Other values (5) 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 572879
12.5%
A 572331
12.5%
C 571018
12.5%
L 570748
12.5%
U 570107
12.5%
R 570096
12.5%
B 569165
12.4%
P 569143
12.4%
D 1872
 
< 0.1%
1867
 
< 0.1%
Other values (16) 9181
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4578407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 572879
12.5%
A 572331
12.5%
C 571018
12.5%
L 570748
12.5%
U 570107
12.5%
R 570096
12.5%
B 569165
12.4%
P 569143
12.4%
D 1872
 
< 0.1%
1867
 
< 0.1%
Other values (16) 9181
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4578407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 572879
12.5%
A 572331
12.5%
C 571018
12.5%
L 570748
12.5%
U 570107
12.5%
R 570096
12.5%
B 569165
12.4%
P 569143
12.4%
D 1872
 
< 0.1%
1867
 
< 0.1%
Other values (16) 9181
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4578407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 572879
12.5%
A 572331
12.5%
C 571018
12.5%
L 570748
12.5%
U 570107
12.5%
R 570096
12.5%
B 569165
12.4%
P 569143
12.4%
D 1872
 
< 0.1%
1867
 
< 0.1%
Other values (16) 9181
 
0.2%

estu_estudiante
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
ESTUDIANTE
570062 
PUBLICAR
 
379
CALDAS
 
23

Length

Max length10
Median length10
Mean length9.99851
Min length6

Characters and Unicode

Total characters5703790
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTUDIANTE
2nd rowESTUDIANTE
3rd rowESTUDIANTE
4th rowESTUDIANTE
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
ESTUDIANTE 570062
99.9%
PUBLICAR 379
 
0.1%
CALDAS 23
 
< 0.1%

Length

2025-05-25T12:56:30.462010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:30.502916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
estudiante 570062
99.9%
publicar 379
 
0.1%
caldas 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 1140124
20.0%
T 1140124
20.0%
A 570487
10.0%
U 570441
10.0%
I 570441
10.0%
S 570085
10.0%
D 570085
10.0%
N 570062
10.0%
L 402
 
< 0.1%
C 402
 
< 0.1%
Other values (3) 1137
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5703790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1140124
20.0%
T 1140124
20.0%
A 570487
10.0%
U 570441
10.0%
I 570441
10.0%
S 570085
10.0%
D 570085
10.0%
N 570062
10.0%
L 402
 
< 0.1%
C 402
 
< 0.1%
Other values (3) 1137
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5703790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1140124
20.0%
T 1140124
20.0%
A 570487
10.0%
U 570441
10.0%
I 570441
10.0%
S 570085
10.0%
D 570085
10.0%
N 570062
10.0%
L 402
 
< 0.1%
C 402
 
< 0.1%
Other values (3) 1137
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5703790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1140124
20.0%
T 1140124
20.0%
A 570487
10.0%
U 570441
10.0%
I 570441
10.0%
S 570085
10.0%
D 570085
10.0%
N 570062
10.0%
L 402
 
< 0.1%
C 402
 
< 0.1%
Other values (3) 1137
 
< 0.1%
Distinct12852
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:30.637857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9999194
Min length8

Characters and Unicode

Total characters5704594
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3737 ?
Unique (%)0.7%

Sample

1st row25/03/1998
2nd row03/08/1998
3rd row14/01/1996
4th row16/10/1998
5th row19/03/1996
ValueCountFrequency (%)
18/09/1998 822
 
0.1%
17/09/1998 821
 
0.1%
21/09/1998 820
 
0.1%
22/09/1998 811
 
0.1%
15/09/1998 797
 
0.1%
02/10/1998 795
 
0.1%
06/10/1998 790
 
0.1%
11/11/1998 782
 
0.1%
18/12/1998 777
 
0.1%
10/09/1998 773
 
0.1%
Other values (12842) 562476
98.6%
2025-05-25T12:56:30.835564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1334625
23.4%
/ 1140124
20.0%
1 1061970
18.6%
0 724970
12.7%
2 345236
 
6.1%
8 330922
 
5.8%
7 228543
 
4.0%
6 158893
 
2.8%
3 137529
 
2.4%
5 126061
 
2.2%
Other values (14) 115721
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5704594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 1334625
23.4%
/ 1140124
20.0%
1 1061970
18.6%
0 724970
12.7%
2 345236
 
6.1%
8 330922
 
5.8%
7 228543
 
4.0%
6 158893
 
2.8%
3 137529
 
2.4%
5 126061
 
2.2%
Other values (14) 115721
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5704594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 1334625
23.4%
/ 1140124
20.0%
1 1061970
18.6%
0 724970
12.7%
2 345236
 
6.1%
8 330922
 
5.8%
7 228543
 
4.0%
6 158893
 
2.8%
3 137529
 
2.4%
5 126061
 
2.2%
Other values (14) 115721
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5704594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 1334625
23.4%
/ 1140124
20.0%
1 1061970
18.6%
0 724970
12.7%
2 345236
 
6.1%
8 330922
 
5.8%
7 228543
 
4.0%
6 158893
 
2.8%
3 137529
 
2.4%
5 126061
 
2.2%
Other values (14) 115721
 
2.0%
Distinct337
Distinct (%)0.1%
Missing2434
Missing (%)0.4%
Memory size4.4 MiB
2025-05-25T12:56:30.932310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.0063694
Min length1

Characters and Unicode

Total characters571648
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)0.1%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowF
ValueCountFrequency (%)
f 310373
54.6%
m 257255
45.3%
estudiante 23
 
< 0.1%
04/10/1997 4
 
< 0.1%
28/01/1997 4
 
< 0.1%
02/05/1998 3
 
< 0.1%
29/01/1997 3
 
< 0.1%
22/11/1997 3
 
< 0.1%
21/12/1997 3
 
< 0.1%
13/09/1998 2
 
< 0.1%
Other values (327) 357
 
0.1%
2025-05-25T12:56:31.069180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 310373
54.3%
M 257255
45.0%
9 854
 
0.1%
/ 758
 
0.1%
1 698
 
0.1%
0 487
 
0.1%
2 220
 
< 0.1%
8 195
 
< 0.1%
7 184
 
< 0.1%
6 127
 
< 0.1%
Other values (11) 497
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 571648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 310373
54.3%
M 257255
45.0%
9 854
 
0.1%
/ 758
 
0.1%
1 698
 
0.1%
0 487
 
0.1%
2 220
 
< 0.1%
8 195
 
< 0.1%
7 184
 
< 0.1%
6 127
 
< 0.1%
Other values (11) 497
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 571648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 310373
54.3%
M 257255
45.0%
9 854
 
0.1%
/ 758
 
0.1%
1 698
 
0.1%
0 487
 
0.1%
2 220
 
< 0.1%
8 195
 
< 0.1%
7 184
 
< 0.1%
6 127
 
< 0.1%
Other values (11) 497
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 571648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 310373
54.3%
M 257255
45.0%
9 854
 
0.1%
/ 758
 
0.1%
1 698
 
0.1%
0 487
 
0.1%
2 220
 
< 0.1%
8 195
 
< 0.1%
7 184
 
< 0.1%
6 127
 
< 0.1%
Other values (11) 497
 
0.1%
Distinct469
Distinct (%)0.1%
Missing88
Missing (%)< 0.1%
Memory size4.4 MiB
2025-05-25T12:56:31.215496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length25
Mean length9.7062552
Min length1

Characters and Unicode

Total characters5536215
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowLA CEJA
2nd rowBOGOTÁ D.C.
3rd rowLA PLATA
4th rowBOGOTÁ D.C.
5th rowMAGANGUÉ
ValueCountFrequency (%)
bogotだ98609
 
12.0%
d.c 98609
 
12.0%
de 37926
 
4.6%
medellãn 29946
 
3.7%
cali 26008
 
3.2%
san 20075
 
2.5%
barranquilla 18990
 
2.3%
cartagena 14561
 
1.8%
indias 14333
 
1.8%
la 11900
 
1.5%
Other values (493) 447950
54.7%
2025-05-25T12:56:31.414095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 756518
 
13.7%
O 406007
 
7.3%
E 306992
 
5.5%
C 306284
 
5.5%
L 300278
 
5.4%
I 285159
 
5.2%
N 280570
 
5.1%
D 260101
 
4.7%
à 259593
 
4.7%
R 258463
 
4.7%
Other values (36) 2116250
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5536215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 756518
 
13.7%
O 406007
 
7.3%
E 306992
 
5.5%
C 306284
 
5.5%
L 300278
 
5.4%
I 285159
 
5.2%
N 280570
 
5.1%
D 260101
 
4.7%
à 259593
 
4.7%
R 258463
 
4.7%
Other values (36) 2116250
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5536215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 756518
 
13.7%
O 406007
 
7.3%
E 306992
 
5.5%
C 306284
 
5.5%
L 300278
 
5.4%
I 285159
 
5.2%
N 280570
 
5.1%
D 260101
 
4.7%
à 259593
 
4.7%
R 258463
 
4.7%
Other values (36) 2116250
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5536215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 756518
 
13.7%
O 406007
 
7.3%
E 306992
 
5.5%
C 306284
 
5.5%
L 300278
 
5.4%
I 285159
 
5.2%
N 280570
 
5.1%
D 260101
 
4.7%
à 259593
 
4.7%
R 258463
 
4.7%
Other values (36) 2116250
38.2%
Distinct1033
Distinct (%)0.2%
Missing5
Missing (%)< 0.1%
Memory size4.4 MiB
2025-05-25T12:56:31.572323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length26
Mean length9.6250283
Min length1

Characters and Unicode

Total characters5490684
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowLA CEJA
2nd rowBOGOTÁ D.C.
3rd rowÍQUIRA
4th rowBOGOTÁ D.C.
5th rowMAGANGUÉ
ValueCountFrequency (%)
bogotだ97975
 
12.0%
d.c 97975
 
12.0%
de 33420
 
4.1%
medellãn 30754
 
3.8%
cali 25889
 
3.2%
san 21535
 
2.6%
barranquilla 17222
 
2.1%
cartagena 13416
 
1.6%
indias 13195
 
1.6%
la 10678
 
1.3%
Other values (1023) 452450
55.5%
2025-05-25T12:56:31.786772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 735163
 
13.4%
O 412684
 
7.5%
E 315754
 
5.8%
L 306834
 
5.6%
C 304781
 
5.6%
N 278834
 
5.1%
I 275776
 
5.0%
R 259255
 
4.7%
D 258016
 
4.7%
à 256983
 
4.7%
Other values (26) 2086604
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5490684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 735163
 
13.4%
O 412684
 
7.5%
E 315754
 
5.8%
L 306834
 
5.6%
C 304781
 
5.6%
N 278834
 
5.1%
I 275776
 
5.0%
R 259255
 
4.7%
D 258016
 
4.7%
à 256983
 
4.7%
Other values (26) 2086604
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5490684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 735163
 
13.4%
O 412684
 
7.5%
E 315754
 
5.8%
L 306834
 
5.6%
C 304781
 
5.6%
N 278834
 
5.1%
I 275776
 
5.0%
R 259255
 
4.7%
D 258016
 
4.7%
à 256983
 
4.7%
Other values (26) 2086604
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5490684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 735163
 
13.4%
O 412684
 
7.5%
E 315754
 
5.8%
L 306834
 
5.6%
C 304781
 
5.6%
N 278834
 
5.1%
I 275776
 
5.0%
R 259255
 
4.7%
D 258016
 
4.7%
à 256983
 
4.7%
Other values (26) 2086604
38.0%
Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:31.861694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length8
Mean length7.9986625
Min length4

Characters and Unicode

Total characters4562949
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 569706
99.8%
cali 294
 
0.1%
venezuela 84
 
< 0.1%
unidos 54
 
< 0.1%
estados 53
 
< 0.1%
neiva 41
 
< 0.1%
del 25
 
< 0.1%
ecuador 23
 
< 0.1%
corea 23
 
< 0.1%
espaã‘a 18
 
< 0.1%
Other values (74) 289
 
0.1%
2025-05-25T12:56:31.984301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1139710
25.0%
A 570511
12.5%
I 570250
12.5%
C 570182
12.5%
L 570171
12.5%
B 569775
12.5%
M 569774
12.5%
E 519
 
< 0.1%
N 308
 
< 0.1%
S 237
 
< 0.1%
Other values (24) 1512
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4562949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1139710
25.0%
A 570511
12.5%
I 570250
12.5%
C 570182
12.5%
L 570171
12.5%
B 569775
12.5%
M 569774
12.5%
E 519
 
< 0.1%
N 308
 
< 0.1%
S 237
 
< 0.1%
Other values (24) 1512
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4562949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1139710
25.0%
A 570511
12.5%
I 570250
12.5%
C 570182
12.5%
L 570171
12.5%
B 569775
12.5%
M 569774
12.5%
E 519
 
< 0.1%
N 308
 
< 0.1%
S 237
 
< 0.1%
Other values (24) 1512
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4562949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1139710
25.0%
A 570511
12.5%
I 570250
12.5%
C 570182
12.5%
L 570171
12.5%
B 569775
12.5%
M 569774
12.5%
E 519
 
< 0.1%
N 308
 
< 0.1%
S 237
 
< 0.1%
Other values (24) 1512
 
< 0.1%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:32.050403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length8
Mean length8.0008765
Min length4

Characters and Unicode

Total characters4564212
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 570085
99.9%
venezuela 84
 
< 0.1%
unidos 54
 
< 0.1%
estados 53
 
< 0.1%
del 24
 
< 0.1%
ecuador 23
 
< 0.1%
corea 23
 
< 0.1%
chinchinだ22
 
< 0.1%
espaã‘a 18
 
< 0.1%
sur 14
 
< 0.1%
Other values (64) 192
 
< 0.1%
2025-05-25T12:56:32.168995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1140417
25.0%
A 570501
12.5%
I 570302
12.5%
C 570250
12.5%
L 570244
12.5%
M 570144
12.5%
B 570122
12.5%
E 467
 
< 0.1%
N 274
 
< 0.1%
S 229
 
< 0.1%
Other values (23) 1262
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4564212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1140417
25.0%
A 570501
12.5%
I 570302
12.5%
C 570250
12.5%
L 570244
12.5%
M 570144
12.5%
B 570122
12.5%
E 467
 
< 0.1%
N 274
 
< 0.1%
S 229
 
< 0.1%
Other values (23) 1262
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4564212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1140417
25.0%
A 570501
12.5%
I 570302
12.5%
C 570250
12.5%
L 570244
12.5%
M 570144
12.5%
B 570122
12.5%
E 467
 
< 0.1%
N 274
 
< 0.1%
S 229
 
< 0.1%
Other values (23) 1262
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4564212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1140417
25.0%
A 570501
12.5%
I 570302
12.5%
C 570250
12.5%
L 570244
12.5%
M 570144
12.5%
B 570122
12.5%
E 467
 
< 0.1%
N 274
 
< 0.1%
S 229
 
< 0.1%
Other values (23) 1262
 
< 0.1%

estu_privado_libertad
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
N
570038 
COLOMBIA
 
402
S
 
24

Length

Max length8
Median length1
Mean length1.0049328
Min length1

Characters and Unicode

Total characters573278
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 570038
99.9%
COLOMBIA 402
 
0.1%
S 24
 
< 0.1%

Length

2025-05-25T12:56:32.219307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:32.259088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
n 570038
99.9%
colombia 402
 
0.1%
s 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 570038
99.4%
O 804
 
0.1%
C 402
 
0.1%
L 402
 
0.1%
M 402
 
0.1%
B 402
 
0.1%
I 402
 
0.1%
A 402
 
0.1%
S 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 573278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 570038
99.4%
O 804
 
0.1%
C 402
 
0.1%
L 402
 
0.1%
M 402
 
0.1%
B 402
 
0.1%
I 402
 
0.1%
A 402
 
0.1%
S 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 573278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 570038
99.4%
O 804
 
0.1%
C 402
 
0.1%
L 402
 
0.1%
M 402
 
0.1%
B 402
 
0.1%
I 402
 
0.1%
A 402
 
0.1%
S 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 573278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 570038
99.4%
O 804
 
0.1%
C 402
 
0.1%
L 402
 
0.1%
M 402
 
0.1%
B 402
 
0.1%
I 402
 
0.1%
A 402
 
0.1%
S 24
 
< 0.1%

fami_cuartoshogar
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing1092
Missing (%)0.2%
Memory size4.4 MiB
Tres
238941 
Dos
203685 
Cuatro
73896 
Uno
 
20950
Cinco
 
20351
Other values (7)
 
11549

Length

Max length11
Median length8
Mean length3.9109159
Min length1

Characters and Unicode

Total characters2226766
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCuatro
2nd rowDos
3rd rowCuatro
4th rowCuatro
5th rowCuatro

Common Values

ValueCountFrequency (%)
Tres 238941
41.9%
Dos 203685
35.7%
Cuatro 73896
 
13.0%
Uno 20950
 
3.7%
Cinco 20351
 
3.6%
Seis 6607
 
1.2%
Siete 2311
 
0.4%
Ocho 1247
 
0.2%
Diez o más 587
 
0.1%
Nueve 395
 
0.1%
Other values (2) 402
 
0.1%
(Missing) 1092
 
0.2%

Length

2025-05-25T12:56:32.299080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tres 238941
41.9%
dos 203685
35.7%
cuatro 73896
 
13.0%
uno 20950
 
3.7%
cinco 20351
 
3.6%
seis 6607
 
1.2%
siete 2311
 
0.4%
ocho 1247
 
0.2%
diez 587
 
0.1%
o 587
 
0.1%
Other values (4) 1384
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s 449820
20.2%
o 320716
14.4%
r 312837
14.0%
e 251547
11.3%
T 238941
10.7%
D 204272
9.2%
C 94270
 
4.2%
t 76207
 
3.4%
u 74291
 
3.3%
a 73896
 
3.3%
Other values (19) 129969
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2226766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 449820
20.2%
o 320716
14.4%
r 312837
14.0%
e 251547
11.3%
T 238941
10.7%
D 204272
9.2%
C 94270
 
4.2%
t 76207
 
3.4%
u 74291
 
3.3%
a 73896
 
3.3%
Other values (19) 129969
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2226766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 449820
20.2%
o 320716
14.4%
r 312837
14.0%
e 251547
11.3%
T 238941
10.7%
D 204272
9.2%
C 94270
 
4.2%
t 76207
 
3.4%
u 74291
 
3.3%
a 73896
 
3.3%
Other values (19) 129969
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2226766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 449820
20.2%
o 320716
14.4%
r 312837
14.0%
e 251547
11.3%
T 238941
10.7%
D 204272
9.2%
C 94270
 
4.2%
t 76207
 
3.4%
u 74291
 
3.3%
a 73896
 
3.3%
Other values (19) 129969
 
5.8%

fami_educacionmadre
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Secundaria (Bachillerato) completa
159677 
Primaria completa
94533 
Secundaria (Bachillerato) incompleta
87234 
Primaria incompleta
84881 
Educación profesional completa
50194 
Other values (16)
92852 

Length

Max length36
Median length33
Mean length27.301253
Min length1

Characters and Unicode

Total characters15544542
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPrimaria completa
2nd rowSecundaria (Bachillerato) incompleta
3rd rowPrimaria incompleta
4th rowSecundaria (Bachillerato) completa
5th rowPrimaria incompleta

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 159677
28.0%
Primaria completa 94533
16.6%
Secundaria (Bachillerato) incompleta 87234
15.3%
Primaria incompleta 84881
14.9%
Educación profesional completa 50194
 
8.8%
Técnica o tecnológica completa 40695
 
7.1%
Ninguno 12596
 
2.2%
Postgrado 11615
 
2.0%
Técnica o tecnológica incompleta 11575
 
2.0%
No sabe 8380
 
1.5%
Other values (11) 7991
 
1.4%

Length

2025-05-25T12:56:32.347779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 345099
22.7%
secundaria 246911
16.2%
bachillerato 246911
16.2%
incompleta 191280
12.6%
primaria 179414
11.8%
educaciã³n 57784
 
3.8%
profesional 57784
 
3.8%
o 52271
 
3.4%
tã©cnica 52270
 
3.4%
tecnolã³gica 52270
 
3.4%
Other values (15) 41373
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 2123057
13.7%
c 1354871
 
8.7%
i 1276666
 
8.2%
e 1148778
 
7.4%
l 1140255
 
7.3%
o 1047843
 
6.7%
953996
 
6.1%
r 922281
 
5.9%
t 847281
 
5.5%
m 715794
 
4.6%
Other values (26) 4013720
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15544542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2123057
13.7%
c 1354871
 
8.7%
i 1276666
 
8.2%
e 1148778
 
7.4%
l 1140255
 
7.3%
o 1047843
 
6.7%
953996
 
6.1%
r 922281
 
5.9%
t 847281
 
5.5%
m 715794
 
4.6%
Other values (26) 4013720
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15544542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2123057
13.7%
c 1354871
 
8.7%
i 1276666
 
8.2%
e 1148778
 
7.4%
l 1140255
 
7.3%
o 1047843
 
6.7%
953996
 
6.1%
r 922281
 
5.9%
t 847281
 
5.5%
m 715794
 
4.6%
Other values (26) 4013720
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15544542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2123057
13.7%
c 1354871
 
8.7%
i 1276666
 
8.2%
e 1148778
 
7.4%
l 1140255
 
7.3%
o 1047843
 
6.7%
953996
 
6.1%
r 922281
 
5.9%
t 847281
 
5.5%
m 715794
 
4.6%
Other values (26) 4013720
25.8%

fami_educacionpadre
Categorical

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Secundaria (Bachillerato) completa
139704 
Primaria incompleta
99985 
Primaria completa
97092 
Secundaria (Bachillerato) incompleta
76463 
Educación profesional completa
47521 
Other values (10)
108606 

Length

Max length36
Median length33
Mean length25.534109
Min length3

Characters and Unicode

Total characters14538381
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPrimaria completa
2nd rowSecundaria (Bachillerato) incompleta
3rd rowSecundaria (Bachillerato) completa
4th rowPrimaria completa
5th rowSecundaria (Bachillerato) completa

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 139704
24.5%
Primaria incompleta 99985
17.5%
Primaria completa 97092
17.0%
Secundaria (Bachillerato) incompleta 76463
13.4%
Educación profesional completa 47521
 
8.3%
Técnica o tecnológica completa 31301
 
5.5%
Ninguno 26107
 
4.6%
No sabe 22290
 
3.9%
Postgrado 12141
 
2.1%
Técnica o tecnológica incompleta 10028
 
1.8%
Other values (5) 6739
 
1.2%

Length

2025-05-25T12:56:32.398134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 315618
21.7%
secundaria 216167
14.9%
bachillerato 216167
14.9%
primaria 197077
13.6%
incompleta 193192
13.3%
educaciã³n 54237
 
3.7%
profesional 54237
 
3.7%
o 41329
 
2.8%
tecnolã³gica 41329
 
2.8%
tã©cnica 41329
 
2.8%
Other values (8) 82851
 
5.7%

Most occurring characters

ValueCountFrequency (%)
a 1993198
13.7%
i 1236920
 
8.5%
c 1214934
 
8.4%
e 1059009
 
7.3%
l 1036710
 
7.1%
o 988803
 
6.8%
r 892876
 
6.1%
884162
 
6.1%
t 778451
 
5.4%
m 705887
 
4.9%
Other values (22) 3747431
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14538381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1993198
13.7%
i 1236920
 
8.5%
c 1214934
 
8.4%
e 1059009
 
7.3%
l 1036710
 
7.1%
o 988803
 
6.8%
r 892876
 
6.1%
884162
 
6.1%
t 778451
 
5.4%
m 705887
 
4.9%
Other values (22) 3747431
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14538381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1993198
13.7%
i 1236920
 
8.5%
c 1214934
 
8.4%
e 1059009
 
7.3%
l 1036710
 
7.1%
o 988803
 
6.8%
r 892876
 
6.1%
884162
 
6.1%
t 778451
 
5.4%
m 705887
 
4.9%
Other values (22) 3747431
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14538381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1993198
13.7%
i 1236920
 
8.5%
c 1214934
 
8.4%
e 1059009
 
7.3%
l 1036710
 
7.1%
o 988803
 
6.8%
r 892876
 
6.1%
884162
 
6.1%
t 778451
 
5.4%
m 705887
 
4.9%
Other values (22) 3747431
25.8%

fami_estratovivienda
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)< 0.1%
Missing136
Missing (%)< 0.1%
Memory size4.4 MiB
Estrato 1
245460 
Estrato 2
194398 
Estrato 3
96095 
Estrato 4
 
21185
Estrato 5
 
7977
Other values (12)
 
5213

Length

Max length36
Median length9
Mean length9.0125454
Min length7

Characters and Unicode

Total characters5140107
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstrato 2
2nd rowEstrato 2
3rd rowEstrato 1
4th rowEstrato 1
5th rowEstrato 1

Common Values

ValueCountFrequency (%)
Estrato 1 245460
43.0%
Estrato 2 194398
34.1%
Estrato 3 96095
 
16.8%
Estrato 4 21185
 
3.7%
Estrato 5 7977
 
1.4%
Estrato 6 4812
 
0.8%
Secundaria (Bachillerato) completa 101
 
< 0.1%
Técnica o tecnológica completa 55
 
< 0.1%
Secundaria (Bachillerato) incompleta 53
 
< 0.1%
Primaria incompleta 51
 
< 0.1%
Other values (7) 141
 
< 0.1%
(Missing) 136
 
< 0.1%

Length

2025-05-25T12:56:32.444902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
estrato 569927
50.0%
1 245460
21.5%
2 194398
 
17.0%
3 96095
 
8.4%
4 21185
 
1.9%
5 7977
 
0.7%
6 4812
 
0.4%
completa 233
 
< 0.1%
secundaria 154
 
< 0.1%
bachillerato 154
 
< 0.1%
Other values (11) 566
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 1140435
22.2%
a 571326
11.1%
o 570695
11.1%
570633
11.1%
r 570466
11.1%
s 569989
11.1%
E 569963
11.1%
1 245460
 
4.8%
2 194398
 
3.8%
3 96095
 
1.9%
Other values (26) 40647
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5140107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1140435
22.2%
a 571326
11.1%
o 570695
11.1%
570633
11.1%
r 570466
11.1%
s 569989
11.1%
E 569963
11.1%
1 245460
 
4.8%
2 194398
 
3.8%
3 96095
 
1.9%
Other values (26) 40647
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5140107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1140435
22.2%
a 571326
11.1%
o 570695
11.1%
570633
11.1%
r 570466
11.1%
s 569989
11.1%
E 569963
11.1%
1 245460
 
4.8%
2 194398
 
3.8%
3 96095
 
1.9%
Other values (26) 40647
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5140107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1140435
22.2%
a 571326
11.1%
o 570695
11.1%
570633
11.1%
r 570466
11.1%
s 569989
11.1%
E 569963
11.1%
1 245460
 
4.8%
2 194398
 
3.8%
3 96095
 
1.9%
Other values (26) 40647
 
0.8%

fami_personashogar
Categorical

High correlation 

Distinct24
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Cuatro
174439 
Cinco
134217 
Tres
100889 
Seis
63861 
Dos
30279 
Other values (19)
65686 

Length

Max length36
Median length34
Mean length4.8981069
Min length3

Characters and Unicode

Total characters2788840
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSiete
2nd rowSeis
3rd rowOcho
4th rowCinco
5th rowSiete

Common Values

ValueCountFrequency (%)
Cuatro 174439
30.6%
Cinco 134217
23.5%
Tres 100889
17.7%
Seis 63861
 
11.2%
Dos 30279
 
5.3%
Siete 29651
 
5.2%
Ocho 15525
 
2.7%
Nueve 6529
 
1.1%
Diez 4492
 
0.8%
Una 3668
 
0.6%
Other values (14) 5821
 
1.0%

Length

2025-05-25T12:56:32.492713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cuatro 174439
30.3%
cinco 134217
23.3%
tres 100889
17.5%
seis 63861
 
11.1%
dos 30279
 
5.3%
siete 29651
 
5.1%
ocho 15525
 
2.7%
nueve 6529
 
1.1%
diez 4492
 
0.8%
una 3668
 
0.6%
Other values (20) 12993
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 361632
13.0%
C 308656
11.1%
r 275742
9.9%
e 247068
8.9%
i 232271
8.3%
t 204877
7.3%
s 198790
7.1%
u 180979
6.5%
a 178568
6.4%
c 155207
 
5.6%
Other values (32) 445050
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2788840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 361632
13.0%
C 308656
11.1%
r 275742
9.9%
e 247068
8.9%
i 232271
8.3%
t 204877
7.3%
s 198790
7.1%
u 180979
6.5%
a 178568
6.4%
c 155207
 
5.6%
Other values (32) 445050
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2788840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 361632
13.0%
C 308656
11.1%
r 275742
9.9%
e 247068
8.9%
i 232271
8.3%
t 204877
7.3%
s 198790
7.1%
u 180979
6.5%
a 178568
6.4%
c 155207
 
5.6%
Other values (32) 445050
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2788840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 361632
13.0%
C 308656
11.1%
r 275742
9.9%
e 247068
8.9%
i 232271
8.3%
t 204877
7.3%
s 198790
7.1%
u 180979
6.5%
a 178568
6.4%
c 155207
 
5.6%
Other values (32) 445050
16.0%

fami_tieneautomovil
Categorical

High correlation  Imbalance 

Distinct16
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
No
446872 
Si
122098 
Cuatro
 
129
Cinco
 
84
Tres
 
65
Other values (11)
 
123

Length

Max length11
Median length2
Mean length2.0022428
Min length2

Characters and Unicode

Total characters1140019
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 446872
78.3%
Si 122098
 
21.4%
Cuatro 129
 
< 0.1%
Cinco 84
 
< 0.1%
Tres 65
 
< 0.1%
Seis 35
 
< 0.1%
Siete 22
 
< 0.1%
Dos 21
 
< 0.1%
Estrato 2 16
 
< 0.1%
Ocho 12
 
< 0.1%
Other values (6) 17
 
< 0.1%
(Missing) 1093
 
0.2%

Length

2025-05-25T12:56:32.537628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 446872
78.5%
si 122098
 
21.4%
cuatro 129
 
< 0.1%
cinco 84
 
< 0.1%
tres 65
 
< 0.1%
seis 35
 
< 0.1%
estrato 23
 
< 0.1%
siete 22
 
< 0.1%
dos 21
 
< 0.1%
2 16
 
< 0.1%
Other values (9) 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 447145
39.2%
N 446875
39.2%
i 122242
 
10.7%
S 122155
 
10.7%
r 217
 
< 0.1%
C 213
 
< 0.1%
t 197
 
< 0.1%
e 157
 
< 0.1%
a 152
 
< 0.1%
s 146
 
< 0.1%
Other values (17) 520
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1140019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 447145
39.2%
N 446875
39.2%
i 122242
 
10.7%
S 122155
 
10.7%
r 217
 
< 0.1%
C 213
 
< 0.1%
t 197
 
< 0.1%
e 157
 
< 0.1%
a 152
 
< 0.1%
s 146
 
< 0.1%
Other values (17) 520
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1140019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 447145
39.2%
N 446875
39.2%
i 122242
 
10.7%
S 122155
 
10.7%
r 217
 
< 0.1%
C 213
 
< 0.1%
t 197
 
< 0.1%
e 157
 
< 0.1%
a 152
 
< 0.1%
s 146
 
< 0.1%
Other values (17) 520
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1140019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 447145
39.2%
N 446875
39.2%
i 122242
 
10.7%
S 122155
 
10.7%
r 217
 
< 0.1%
C 213
 
< 0.1%
t 197
 
< 0.1%
e 157
 
< 0.1%
a 152
 
< 0.1%
s 146
 
< 0.1%
Other values (17) 520
 
< 0.1%

fami_tienecomputador
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Si
351056 
No
218292 
Tres
 
8
Cinco
 
6
Dos
 
4
Other values (2)
 
5

Length

Max length6
Median length2
Mean length2.0000913
Min length2

Characters and Unicode

Total characters1138794
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSi
3rd rowSi
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si 351056
61.5%
No 218292
38.3%
Tres 8
 
< 0.1%
Cinco 6
 
< 0.1%
Dos 4
 
< 0.1%
Seis 3
 
< 0.1%
Cuatro 2
 
< 0.1%
(Missing) 1093
 
0.2%

Length

2025-05-25T12:56:32.647278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:32.692604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
si 351056
61.7%
no 218292
38.3%
tres 8
 
< 0.1%
cinco 6
 
< 0.1%
dos 4
 
< 0.1%
seis 3
 
< 0.1%
cuatro 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 351065
30.8%
S 351059
30.8%
o 218304
19.2%
N 218292
19.2%
s 15
 
< 0.1%
e 11
 
< 0.1%
r 10
 
< 0.1%
T 8
 
< 0.1%
C 8
 
< 0.1%
n 6
 
< 0.1%
Other values (5) 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 351065
30.8%
S 351059
30.8%
o 218304
19.2%
N 218292
19.2%
s 15
 
< 0.1%
e 11
 
< 0.1%
r 10
 
< 0.1%
T 8
 
< 0.1%
C 8
 
< 0.1%
n 6
 
< 0.1%
Other values (5) 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 351065
30.8%
S 351059
30.8%
o 218304
19.2%
N 218292
19.2%
s 15
 
< 0.1%
e 11
 
< 0.1%
r 10
 
< 0.1%
T 8
 
< 0.1%
C 8
 
< 0.1%
n 6
 
< 0.1%
Other values (5) 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 351065
30.8%
S 351059
30.8%
o 218304
19.2%
N 218292
19.2%
s 15
 
< 0.1%
e 11
 
< 0.1%
r 10
 
< 0.1%
T 8
 
< 0.1%
C 8
 
< 0.1%
n 6
 
< 0.1%
Other values (5) 16
 
< 0.1%

fami_tieneinternet
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Si
302531 
No
266840 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1138742
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSi
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si 302531
53.0%
No 266840
46.8%
(Missing) 1093
 
0.2%

Length

2025-05-25T12:56:32.738451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:32.773085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
si 302531
53.1%
no 266840
46.9%

Most occurring characters

ValueCountFrequency (%)
S 302531
26.6%
i 302531
26.6%
N 266840
23.4%
o 266840
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 302531
26.6%
i 302531
26.6%
N 266840
23.4%
o 266840
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 302531
26.6%
i 302531
26.6%
N 266840
23.4%
o 266840
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 302531
26.6%
i 302531
26.6%
N 266840
23.4%
o 266840
23.4%
Distinct2
Distinct (%)< 0.1%
Missing1093
Missing (%)0.2%
Memory size4.4 MiB
Si
385118 
No
184253 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1138742
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowNo
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 385118
67.5%
No 184253
32.3%
(Missing) 1093
 
0.2%

Length

2025-05-25T12:56:32.811925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:32.846462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
si 385118
67.6%
no 184253
32.4%

Most occurring characters

ValueCountFrequency (%)
S 385118
33.8%
i 385118
33.8%
N 184253
16.2%
o 184253
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 385118
33.8%
i 385118
33.8%
N 184253
16.2%
o 184253
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 385118
33.8%
i 385118
33.8%
N 184253
16.2%
o 184253
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 385118
33.8%
i 385118
33.8%
N 184253
16.2%
o 184253
16.2%

desemp_ingles
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.4 MiB
A-
257395 
A1
219588 
A2
46919 
B1
31953 
B+
 
14207
Other values (2)
 
401

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1140926
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA1
2nd rowA-
3rd rowA-
4th rowA-
5th rowA1

Common Values

ValueCountFrequency (%)
A- 257395
45.1%
A1 219588
38.5%
A2 46919
 
8.2%
B1 31953
 
5.6%
B+ 14207
 
2.5%
Si 295
 
0.1%
No 106
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-05-25T12:56:32.885084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T12:56:32.925848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
a 257395
45.1%
a1 219588
38.5%
a2 46919
 
8.2%
b1 31953
 
5.6%
b 14207
 
2.5%
si 295
 
0.1%
no 106
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 523902
45.9%
- 257395
22.6%
1 251541
22.0%
2 46919
 
4.1%
B 46160
 
4.0%
+ 14207
 
1.2%
S 295
 
< 0.1%
i 295
 
< 0.1%
N 106
 
< 0.1%
o 106
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1140926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 523902
45.9%
- 257395
22.6%
1 251541
22.0%
2 46919
 
4.1%
B 46160
 
4.0%
+ 14207
 
1.2%
S 295
 
< 0.1%
i 295
 
< 0.1%
N 106
 
< 0.1%
o 106
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1140926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 523902
45.9%
- 257395
22.6%
1 251541
22.0%
2 46919
 
4.1%
B 46160
 
4.0%
+ 14207
 
1.2%
S 295
 
< 0.1%
i 295
 
< 0.1%
N 106
 
< 0.1%
o 106
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1140926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 523902
45.9%
- 257395
22.6%
1 251541
22.0%
2 46919
 
4.1%
B 46160
 
4.0%
+ 14207
 
1.2%
S 295
 
< 0.1%
i 295
 
< 0.1%
N 106
 
< 0.1%
o 106
 
< 0.1%

punt_ingles
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)< 0.1%
Missing402
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.727858
Minimum0
Maximum100
Zeros222
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:32.978005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q143
median49
Q354
95-th percentile76
Maximum100
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.463933
Coefficient of variation (CV)0.22598891
Kurtosis2.8755544
Mean50.727858
Median Absolute Deviation (MAD)6
Skewness1.4648796
Sum28918024
Variance131.42177
MonotonicityNot monotonic
2025-05-25T12:56:33.032962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 49474
 
8.7%
49 49409
 
8.7%
44 48248
 
8.5%
43 45018
 
7.9%
50 42798
 
7.5%
41 37022
 
6.5%
52 35851
 
6.3%
53 28950
 
5.1%
40 28337
 
5.0%
54 22316
 
3.9%
Other values (63) 182639
32.0%
ValueCountFrequency (%)
0 222
< 0.1%
7 48
 
< 0.1%
14 76
 
< 0.1%
17 2
 
< 0.1%
19 7
 
< 0.1%
20 141
 
< 0.1%
21 1
 
< 0.1%
23 8
 
< 0.1%
24 2
 
< 0.1%
25 420
0.1%
ValueCountFrequency (%)
100 631
 
0.1%
97 1029
 
0.2%
95 875
 
0.2%
94 1628
0.3%
92 31
 
< 0.1%
91 2158
0.4%
90 1215
 
0.2%
88 2591
0.5%
87 43
 
< 0.1%
85 4006
0.7%

punt_matematicas
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)< 0.1%
Missing23
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean50.357097
Minimum0
Maximum100
Zeros55
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:33.086224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q142
median49
Q357
95-th percentile72
Maximum100
Range100
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.269154
Coefficient of variation (CV)0.24364299
Kurtosis1.0811842
Mean50.357097
Median Absolute Deviation (MAD)7
Skewness0.67501379
Sum28725753
Variance150.53214
MonotonicityNot monotonic
2025-05-25T12:56:33.141278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 33584
 
5.9%
49 32657
 
5.7%
44 31588
 
5.5%
52 30700
 
5.4%
42 29058
 
5.1%
54 26868
 
4.7%
46 25792
 
4.5%
55 25556
 
4.5%
50 25472
 
4.5%
57 21673
 
3.8%
Other values (85) 287493
50.4%
ValueCountFrequency (%)
0 55
< 0.1%
1 9
 
< 0.1%
2 3
 
< 0.1%
7 19
 
< 0.1%
8 30
< 0.1%
10 3
 
< 0.1%
11 4
 
< 0.1%
12 49
< 0.1%
13 74
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
100 1378
0.2%
99 24
 
< 0.1%
97 1
 
< 0.1%
96 686
0.1%
95 134
 
< 0.1%
94 2
 
< 0.1%
93 700
0.1%
92 342
 
0.1%
91 126
 
< 0.1%
90 591
0.1%

punt_sociales_ciudadanas
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.050902
Minimum0
Maximum100
Zeros55
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:33.194461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q143
median50
Q357
95-th percentile68
Maximum100
Range100
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.420422
Coefficient of variation (CV)0.22817614
Kurtosis0.20945276
Mean50.050902
Median Absolute Deviation (MAD)7
Skewness0.15330795
Sum28552238
Variance130.42603
MonotonicityNot monotonic
2025-05-25T12:56:33.249043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 27994
 
4.9%
53 27017
 
4.7%
49 27008
 
4.7%
46 26273
 
4.6%
43 24636
 
4.3%
56 24383
 
4.3%
47 21247
 
3.7%
52 20852
 
3.7%
59 20834
 
3.7%
38 20415
 
3.6%
Other values (87) 329805
57.8%
ValueCountFrequency (%)
0 55
< 0.1%
3 38
 
< 0.1%
4 10
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
9 95
< 0.1%
10 45
< 0.1%
11 11
 
< 0.1%
12 12
 
< 0.1%
ValueCountFrequency (%)
100 96
< 0.1%
99 24
 
< 0.1%
98 100
< 0.1%
97 44
 
< 0.1%
96 32
 
< 0.1%
95 5
 
< 0.1%
94 2
 
< 0.1%
93 46
 
< 0.1%
92 212
< 0.1%
91 138
< 0.1%

punt_c_naturales
Real number (ℝ)

High correlation 

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.276589
Minimum0
Maximum100
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:33.302431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q143
median50
Q356
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.401071
Coefficient of variation (CV)0.20687703
Kurtosis0.83948836
Mean50.276589
Median Absolute Deviation (MAD)7
Skewness0.50847104
Sum28680984
Variance108.18228
MonotonicityNot monotonic
2025-05-25T12:56:33.355971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 25869
 
4.5%
48 25332
 
4.4%
44 25200
 
4.4%
42 23866
 
4.2%
50 23841
 
4.2%
51 22939
 
4.0%
52 21876
 
3.8%
53 20589
 
3.6%
46 20096
 
3.5%
45 19611
 
3.4%
Other values (77) 341245
59.8%
ValueCountFrequency (%)
0 70
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
7 10
 
< 0.1%
8 43
< 0.1%
10 1
 
< 0.1%
13 47
< 0.1%
14 56
< 0.1%
15 2
 
< 0.1%
16 21
 
< 0.1%
ValueCountFrequency (%)
100 181
 
< 0.1%
96 149
 
< 0.1%
95 129
 
< 0.1%
93 113
 
< 0.1%
91 118
 
< 0.1%
90 353
0.1%
89 31
 
< 0.1%
88 7
 
< 0.1%
87 426
0.1%
86 499
0.1%

punt_lectura_critica
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.923127
Minimum0
Maximum100
Zeros220
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:33.408548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q143
median49
Q356
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.5330995
Coefficient of variation (CV)0.19095558
Kurtosis0.76379482
Mean49.923127
Median Absolute Deviation (MAD)6
Skewness0.34803158
Sum28479347
Variance90.879987
MonotonicityNot monotonic
2025-05-25T12:56:33.463461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 35042
 
6.1%
47 33774
 
5.9%
50 32704
 
5.7%
43 31965
 
5.6%
53 29349
 
5.1%
54 27722
 
4.9%
49 26897
 
4.7%
51 24736
 
4.3%
44 24240
 
4.2%
40 23884
 
4.2%
Other values (71) 280151
49.1%
ValueCountFrequency (%)
0 220
< 0.1%
4 2
 
< 0.1%
7 8
 
< 0.1%
8 34
 
< 0.1%
9 4
 
< 0.1%
10 9
 
< 0.1%
11 4
 
< 0.1%
16 137
< 0.1%
17 2
 
< 0.1%
18 17
 
< 0.1%
ValueCountFrequency (%)
100 55
 
< 0.1%
99 5
 
< 0.1%
94 70
 
< 0.1%
93 208
 
< 0.1%
92 4
 
< 0.1%
91 18
 
< 0.1%
86 77
 
< 0.1%
85 721
0.1%
84 15
 
< 0.1%
81 232
 
< 0.1%

punt_global
Real number (ℝ)

High correlation 

Distinct471
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.84173
Minimum0
Maximum492
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-25T12:56:33.516332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile182
Q1216
median246
Q3280
95-th percentile340
Maximum492
Range492
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.752089
Coefficient of variation (CV)0.19435398
Kurtosis0.74577018
Mean250.84173
Median Absolute Deviation (MAD)31
Skewness0.58557428
Sum1.4309618 × 108
Variance2376.7662
MonotonicityNot monotonic
2025-05-25T12:56:33.570239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 5820
 
1.0%
232 5779
 
1.0%
242 5740
 
1.0%
243 5699
 
1.0%
245 5681
 
1.0%
235 5668
 
1.0%
228 5647
 
1.0%
237 5641
 
1.0%
248 5616
 
1.0%
238 5604
 
1.0%
Other values (461) 513569
90.0%
ValueCountFrequency (%)
0 4
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
12 2
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
492 1
 
< 0.1%
490 1
 
< 0.1%
488 1
 
< 0.1%
483 2
< 0.1%
479 1
 
< 0.1%
478 2
< 0.1%
477 3
< 0.1%
476 4
< 0.1%
475 1
 
< 0.1%
473 2
< 0.1%

Interactions

2025-05-25T12:56:19.963040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:05.925127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.880783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.964820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.921990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.918324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.892303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.884954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.010256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.927739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.015936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.032978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.026326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.053358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.000769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.023613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.004009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.941663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.025636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.980294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.976521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.955714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.949935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.072146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.996109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.101200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.095916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.090515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.115958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.061703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.084883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.070521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.005654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.091988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.047973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.043702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.041868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.020220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.133788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.064936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.177416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.165422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.151626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.181359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.128637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.145634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.133959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.179866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.148665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.105091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.105844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.109019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.153849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.190773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.132435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.257438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.229440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.217530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.243221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.188455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.203034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.196739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.240380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.211589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.161774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.167581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.174400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.216520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.250247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.195141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.330627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.303267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.279267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.300511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.252367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.338447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.263003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.304680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.282097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.227462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.236698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.240638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.284658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.310508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.262191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.398902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.379621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.348476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.367150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.322096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.399660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.321999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.368496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.343712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.288085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.304757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.302391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.346019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.369721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.322836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.461250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.438587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.411059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.435404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.385416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.462979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.382323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.432207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.409017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.351082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.371204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.368389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.411549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.434812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.382682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.521218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.502269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.538018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.498818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.451101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.526743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.442991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.504204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.474525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.416457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.438513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.433022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.476367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.508473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.442752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.586041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.564621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.604370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.563451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.515594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.590436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.505660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.572184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.544273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.541338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.504815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.497601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.600393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.566336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.507858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.653886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.633451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.668687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.628603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.582820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.655776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.568023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.643804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.608517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.606654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.572607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.561280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.680630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.621843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.569089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.717830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.697930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.737688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.695431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.647365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.719763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.632191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.707668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.670653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.669381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.636264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.626262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.749415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.682435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.634609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.781501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.770854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.798485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.758406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.710497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.783053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.693324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.771616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.733269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.731279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.701812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.691470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.818220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.741991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.701529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.846045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.835248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.862742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.816488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.778542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.845867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.755011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.836078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.795197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.795646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.767896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.755600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.886077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.801652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.841966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.909928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.902021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.925721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.877485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.838153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:20.908576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:06.818985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:07.899514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:08.856112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:09.853878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:10.829078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:11.821892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:12.954070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:13.863124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:14.919174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:15.971406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:16.963651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:17.990107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:18.939179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T12:56:19.900390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-25T12:56:33.695460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
cole_area_ubicacioncole_bilinguecole_calendariocole_caractercole_cod_dane_establecimientocole_cod_dane_sedecole_cod_depto_ubicacioncole_cod_mcpio_ubicacioncole_codigo_icfescole_depto_ubicacioncole_generocole_jornadacole_naturalezacole_sede_principaldesemp_inglesestu_cod_depto_presentacionestu_cod_mcpio_presentacionestu_cod_reside_deptoestu_cod_reside_mcpioestu_depto_presentacionestu_depto_resideestu_estadoinvestigacionestu_estudianteestu_privado_libertadestu_tipodocumentofami_cuartoshogarfami_educacionmadrefami_educacionpadrefami_estratoviviendafami_personashogarfami_tieneautomovilfami_tienecomputadorfami_tieneinternetfami_tienelavadoraperiodopunt_c_naturalespunt_globalpunt_inglespunt_lectura_criticapunt_matematicaspunt_sociales_ciudadanas
cole_area_ubicacion1.0000.0370.0280.1170.8200.6360.1960.1900.1940.2560.0660.1240.1630.0590.1110.1860.1320.0060.1360.2430.2450.0150.0060.0060.0370.0540.2360.2340.2640.0820.0880.2420.2740.2240.0300.1120.1340.1140.1310.1220.116
cole_bilingue0.0371.0000.3170.0470.1000.0920.0620.0610.0580.1410.0070.1200.0890.0020.2250.0630.0710.0030.0720.1450.1430.0040.0030.0030.0360.0300.1540.1750.3140.0170.1010.0220.0240.0070.2440.1660.1780.2340.1280.1360.152
cole_calendario0.0280.3171.0000.0850.2540.2430.1700.1700.0500.2150.0520.1290.2920.0630.1980.1650.1500.0340.1570.2120.2140.0350.0300.0290.0660.0530.1870.1980.3160.0590.1460.0760.1270.0890.6990.1910.1830.2050.1480.1360.167
cole_caracter0.1170.0470.0851.0000.2430.2120.1630.1620.1210.2740.0340.1070.3700.0410.0840.1630.1570.0100.1620.2730.2740.0140.0100.0090.0230.0190.0810.0850.1200.0260.0720.0520.1260.1070.1240.0550.0630.0840.0480.0520.050
cole_cod_dane_establecimiento0.8200.1000.2540.2431.0000.9940.3610.3580.1380.3770.0950.2200.9210.0440.1320.3530.3480.3550.3490.3680.3730.0190.0340.0330.0260.0430.1360.1340.1600.0550.0990.1270.3620.2820.3300.0630.0640.1070.0570.0570.061
cole_cod_dane_sede0.6360.0920.2430.2120.9941.0000.3600.3570.1400.2780.0800.2000.8540.0350.1160.3520.3470.3540.3480.2710.2730.0200.0320.0300.0300.0430.1330.1290.1590.0550.1000.1020.2990.2090.3130.0610.0630.1060.0560.0560.060
cole_cod_depto_ubicacion0.1960.0620.1700.1630.3610.3601.0000.9980.0171.0000.0780.1290.2050.0380.0590.9860.9810.9920.9880.9790.9890.0280.0420.0400.0210.0300.0590.0550.1120.0360.0390.0930.2930.2420.158-0.018-0.040-0.056-0.046-0.039-0.032
cole_cod_mcpio_ubicacion0.1900.0610.1700.1620.3580.3570.9981.0000.0180.9840.0750.1280.2020.0370.0580.9850.9830.9910.9900.9630.9730.0280.0420.0400.0200.0300.0600.0560.1100.0360.0380.0930.2900.2420.158-0.022-0.045-0.061-0.051-0.043-0.036
cole_codigo_icfes0.1940.0580.0500.1210.1380.1400.0170.0181.0000.0920.1290.1780.0980.0370.0920.0160.0160.0170.0160.0900.0890.0110.0120.0120.0890.0240.1010.0900.0840.0290.0570.0630.1070.0880.026-0.238-0.260-0.196-0.221-0.238-0.228
cole_depto_ubicacion0.2560.1410.2150.2740.3770.2781.0000.9840.0921.0000.1130.1890.2750.0500.0930.9790.9790.0410.9710.9820.9920.0350.0570.0490.0420.0520.0580.0690.1280.0430.0550.1370.3770.3220.2120.0830.0880.0740.0740.0770.074
cole_genero0.0660.0070.0520.0340.0950.0800.0780.0750.1290.1131.0000.1070.1170.0210.1390.0780.0810.0020.0800.1110.1110.0020.0030.0030.0470.0310.1400.1340.1520.0340.0960.0760.1240.0830.0330.1180.1310.1390.1130.1180.112
cole_jornada0.1240.1200.1290.1070.2200.2000.1290.1280.1780.1890.1071.0000.4460.0340.1610.1290.1270.0160.1250.1900.1900.0200.0280.0260.2000.0480.1560.1450.1540.0520.1160.0840.1800.1240.1480.1660.1790.1630.1440.1590.149
cole_naturaleza0.1630.0890.2920.3700.9210.8540.2050.2020.0980.2750.1170.4461.0000.0970.3130.2090.1620.0210.1610.2780.2780.0250.0210.0230.0490.1170.3600.3600.4410.1540.3080.2460.2980.2050.3140.2270.2490.3140.2050.2100.201
cole_sede_principal0.0590.0020.0630.0410.0440.0350.0380.0370.0370.0500.0210.0340.0971.0000.4510.0810.0280.7470.0280.8180.3370.7471.0000.7070.0080.4710.5230.3340.4700.5360.4990.4090.0470.0390.0380.0130.3090.0350.0140.0140.013
desemp_ingles0.1110.2250.1980.0840.1320.1160.0590.0580.0920.0930.1390.1610.3130.4511.0000.0700.0600.4460.0580.4580.1500.4360.7140.7070.0800.4140.4740.2420.4850.4530.4420.1660.3040.2080.1920.3030.5010.8150.2850.2900.281
estu_cod_depto_presentacion0.1860.0630.1650.1630.3530.3520.9860.9850.0160.9790.0780.1290.2090.0810.0701.0000.9980.9890.9871.0000.9820.0081.0000.0070.0210.0220.0570.0570.1490.0290.0990.2290.2950.2430.158-0.020-0.042-0.060-0.049-0.042-0.035
estu_cod_mcpio_presentacion0.1320.0710.1500.1570.3480.3470.9810.9830.0160.9790.0810.1270.1620.0280.0600.9981.0000.9830.9891.0000.9820.0190.0530.0380.0260.0330.0540.0500.0980.0370.0420.1720.2220.1740.147-0.024-0.044-0.064-0.052-0.045-0.038
estu_cod_reside_depto0.0060.0030.0340.0100.3550.3540.9920.9910.0170.0410.0020.0160.0210.7470.4460.9890.9831.0000.9940.8750.4490.8730.7060.6860.0490.4470.4750.0120.5290.4920.4760.0080.0120.0050.013-0.022-0.045-0.061-0.050-0.043-0.036
estu_cod_reside_mcpio0.1360.0720.1570.1620.3490.3480.9880.9900.0160.9710.0800.1250.1610.0280.0580.9870.9890.9941.0000.9640.9810.0200.0380.0370.0260.0310.0570.0530.0950.0340.0410.0680.2210.1810.150-0.026-0.046-0.066-0.054-0.047-0.040
estu_depto_presentacion0.2430.1450.2120.2730.3680.2710.9790.9630.0900.9820.1110.1900.2780.8180.4581.0001.0000.8750.9641.0000.9540.8821.0000.7070.0430.4290.3490.2760.3620.3280.3930.4300.3790.3230.2140.0840.3020.0770.0750.0780.075
estu_depto_reside0.2450.1430.2140.2740.3730.2730.9890.9730.0890.9920.1110.1900.2780.3370.1500.9820.9820.4490.9810.9541.0000.3350.7090.1760.0560.3060.2310.2810.1540.2340.2660.4550.3790.3240.2140.0850.1210.0770.0750.0780.075
estu_estadoinvestigacion0.0150.0040.0350.0140.0190.0200.0280.0280.0110.0350.0020.0200.0250.7470.4360.0080.0190.8730.0200.8820.3351.0001.0000.7070.0120.4710.5000.3330.4370.5120.4970.4080.0180.0110.0190.0050.2890.0050.0100.0040.004
estu_estudiante0.0060.0030.0300.0100.0340.0320.0420.0420.0120.0570.0030.0280.0211.0000.7141.0000.0530.7060.0381.0000.7091.0001.0000.7070.0071.0001.0000.7070.7221.0001.0000.7070.0130.0010.0140.0070.6001.0000.0080.0110.006
estu_privado_libertad0.0060.0030.0290.0090.0330.0300.0400.0400.0120.0490.0030.0260.0230.7070.7070.0070.0380.6860.0370.7070.1760.7070.7071.0000.0100.7070.7070.1700.7070.7070.7070.1700.0100.0020.0190.0060.5950.0040.0070.0080.004
estu_tipodocumento0.0370.0360.0660.0230.0260.0300.0210.0200.0890.0420.0470.2000.0490.0080.0800.0210.0260.0490.0260.0430.0560.0120.0070.0101.0000.0230.0800.0680.0440.0250.0350.0660.1440.1160.0980.0900.0970.0670.0820.0890.079
fami_cuartoshogar0.0540.0300.0530.0190.0430.0430.0300.0300.0240.0520.0310.0480.1170.4710.4140.0220.0330.4470.0310.4290.3060.4711.0000.7070.0231.0000.4290.3050.3140.4640.4290.4160.1970.1700.0520.0300.2850.0340.0290.0310.028
fami_educacionmadre0.2360.1540.1870.0810.1360.1330.0590.0600.1010.0580.1400.1560.3600.5230.4740.0570.0540.4750.0570.3490.2310.5001.0000.7070.0800.4291.0000.4210.3310.3370.5090.4400.4200.3390.1900.1460.3290.1690.1390.1430.139
fami_educacionpadre0.2340.1750.1980.0850.1340.1290.0550.0560.0900.0690.1340.1450.3600.3340.2420.0570.0500.0120.0530.2760.2810.3330.7070.1700.0680.3050.4211.0000.1930.3660.3010.5700.4020.3200.2070.1430.1790.1700.1340.1390.135
fami_estratovivienda0.2640.3140.3160.1200.1600.1590.1120.1100.0840.1280.1520.1540.4410.4700.4850.1490.0980.5290.0950.3620.1540.4370.7220.7070.0440.3140.3310.1931.0000.3110.3270.2620.5230.3830.3380.1470.3360.2590.1370.1410.139
fami_personashogar0.0820.0170.0590.0260.0550.0550.0360.0360.0290.0430.0340.0520.1540.5360.4530.0290.0370.4920.0340.3280.2340.5121.0000.7070.0250.4640.3370.3660.3111.0000.4090.5410.1340.0950.0750.0440.2890.0460.0450.0420.045
fami_tieneautomovil0.0880.1010.1460.0720.0990.1000.0390.0380.0570.0550.0960.1160.3080.4990.4420.0990.0420.4760.0410.3930.2660.4971.0000.7070.0350.4290.5090.3010.3270.4091.0000.4730.3380.2840.1590.0900.3030.3330.0830.0890.083
fami_tienecomputador0.2420.0220.0760.0520.1270.1020.0930.0930.0630.1370.0760.0840.2460.4090.1660.2290.1720.0080.0680.4300.4550.4080.7070.1700.0660.4160.4400.5700.2620.5410.4731.0000.6860.4520.0950.1120.1610.2810.1140.2730.109
fami_tieneinternet0.2740.0240.1270.1260.3620.2990.2930.2900.1070.3770.1240.1800.2980.0470.3040.2950.2220.0120.2210.3790.3790.0180.0130.0100.1440.1970.4200.4020.5230.1340.3380.6861.0000.4510.1130.2790.3110.3070.2870.2770.270
fami_tienelavadora0.2240.0070.0890.1070.2820.2090.2420.2420.0880.3220.0830.1240.2050.0390.2080.2430.1740.0050.1810.3230.3240.0110.0010.0020.1160.1700.3390.3200.3830.0950.2840.4520.4511.0000.0770.1880.2130.2100.1980.1920.181
periodo0.0300.2440.6990.1240.3300.3130.1580.1580.0260.2120.0330.1480.3140.0380.1920.1580.1470.0130.1500.2140.2140.0190.0140.0190.0980.0520.1900.2070.3380.0750.1590.0950.1130.0771.0000.2140.1730.2040.1370.1170.167
punt_c_naturales0.1120.1660.1910.0550.0630.061-0.018-0.022-0.2380.0830.1180.1660.2270.0130.303-0.020-0.024-0.022-0.0260.0840.0850.0050.0070.0060.0900.0300.1460.1430.1470.0440.0900.1120.2790.1880.2141.0000.9000.5620.7280.7620.767
punt_global0.1340.1780.1830.0630.0640.063-0.040-0.045-0.2600.0880.1310.1790.2490.3090.501-0.042-0.044-0.045-0.0460.3020.1210.2890.6000.5950.0970.2850.3290.1790.3360.2890.3030.1610.3110.2130.1730.9001.0000.6460.8760.8870.907
punt_ingles0.1140.2340.2050.0840.1070.106-0.056-0.061-0.1960.0740.1390.1630.3140.0350.815-0.060-0.064-0.061-0.0660.0770.0770.0051.0000.0040.0670.0340.1690.1700.2590.0460.3330.2810.3070.2100.2040.5620.6461.0000.5470.5370.548
punt_lectura_critica0.1310.1280.1480.0480.0570.056-0.046-0.051-0.2210.0740.1130.1440.2050.0140.285-0.049-0.052-0.050-0.0540.0750.0750.0100.0080.0070.0820.0290.1390.1340.1370.0450.0830.1140.2870.1980.1370.7280.8760.5471.0000.7010.767
punt_matematicas0.1220.1360.1360.0520.0570.056-0.039-0.043-0.2380.0770.1180.1590.2100.0140.290-0.042-0.045-0.043-0.0470.0780.0780.0040.0110.0080.0890.0310.1430.1390.1410.0420.0890.2730.2770.1920.1170.7620.8870.5370.7011.0000.721
punt_sociales_ciudadanas0.1160.1520.1670.0500.0610.060-0.032-0.036-0.2280.0740.1120.1490.2010.0130.281-0.035-0.038-0.036-0.0400.0750.0750.0040.0060.0040.0790.0280.1390.1350.1390.0450.0830.1090.2700.1810.1670.7670.9070.5480.7670.7211.000

Missing values

2025-05-25T12:56:21.421440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T12:56:22.945645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-25T12:56:25.729892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

periodoestu_tipodocumentoestu_consecutivocole_area_ubicacioncole_bilinguecole_calendariocole_caractercole_cod_dane_establecimientocole_cod_dane_sedecole_cod_depto_ubicacioncole_cod_mcpio_ubicacioncole_codigo_icfescole_depto_ubicacioncole_generocole_jornadacole_mcpio_ubicacioncole_naturalezacole_nombre_establecimientocole_nombre_sedecole_sede_principalestu_cod_depto_presentacionestu_cod_mcpio_presentacionestu_cod_reside_deptoestu_cod_reside_mcpioestu_depto_presentacionestu_depto_resideestu_estadoinvestigacionestu_estudianteestu_fechanacimientoestu_generoestu_mcpio_presentacionestu_mcpio_resideestu_nacionalidadestu_pais_resideestu_privado_libertadfami_cuartoshogarfami_educacionmadrefami_educacionpadrefami_estratoviviendafami_personashogarfami_tieneautomovilfami_tienecomputadorfami_tieneinternetfami_tienelavadoradesemp_inglespunt_inglespunt_matematicaspunt_sociales_ciudadanaspunt_c_naturalespunt_lectura_criticapunt_global
020152TISB11201520043269URBANONAACADÉMICO1.053760e+1110537600011355376121590ANTIOQUIAMIXTOTARDELA CEJAOFICIALI. E. MONSEÑOR ALFONSO URIBE JARAMILLOI. E. MONSEÑOR ALFONSO URIBE JARAMILLOS5.05376.05.05376.0ANTIOQUIAANTIOQUIAPUBLICARESTUDIANTE25/03/1998MLA CEJALA CEJACOLOMBIACOLOMBIANCuatroPrimaria completaPrimaria completaEstrato 2SieteNoNoNoSiA149.052.0494552247
120152TISB11201520486210URBANONATÉCNICO/ACADÉMICO1.110010e+11111001019411111100126138BOGOTAMIXTOMAÑANABOGOTÁ D.C.OFICIALCOLEGIO INEM SANTIAGO PEREZ (IED)INEM SANTIAGO PEREZS11.011001.011.011001.0BOGOTÁBOGOTÁPUBLICARESTUDIANTE03/08/1998MBOGOTÁ D.C.BOGOTÁ D.C.COLOMBIACOLOMBIANDosSecundaria (Bachillerato) incompletaSecundaria (Bachillerato) incompletaEstrato 2SeisNoSiSiSiA-44.072.0606259309
220152TISB11201520344372RURALNaNAACADÉMICO2.413570e+112413570006934141357126516HUILAMIXTOCOMPLETAIQUIRAOFICIALINSTITUCION EDUCATIVA KUE DSI JKUE DSI JS41.041396.041.041357.0HUILAHUILAPUBLICARESTUDIANTE14/01/1996MLA PLATAÍQUIRACOLOMBIACOLOMBIANCuatroPrimaria incompletaSecundaria (Bachillerato) completaEstrato 1OchoNoSiNoNoA-44.046.0384238206
320152TISB11201520036842URBANONaNAACADÉMICO3.110011e+113110010897101111001189365BOGOTAMIXTOCOMPLETABOGOTÁ D.C.NO OFICIALCOLEGIO SOLES DEL SABERCOL SOLES DEL SABERS11.011001.011.011001.0BOGOTÁBOGOTÁPUBLICARESTUDIANTE16/10/1998MBOGOTÁ D.C.BOGOTÁ D.C.COLOMBIACOLOMBIANCuatroSecundaria (Bachillerato) completaPrimaria completaEstrato 1CincoNoSiSiSiA-44.047.0574758258
420152TISB11201520335295RURALNAACADÉMICO2.134300e+112134300026121313430133470BOLIVARMIXTOMAÑANAMAGANGUEOFICIALINST EDUC SAN SEBASTIAN DE MADRIDSAN SEBASTIAN DE MADRIDS13.013430.013.013430.0BOLIVARBOLIVARPUBLICARESTUDIANTE19/03/1996FMAGANGUÉMAGANGUÉCOLOMBIACOLOMBIANCuatroPrimaria incompletaSecundaria (Bachillerato) completaEstrato 1SieteNoNoNoSiA155.044.0455341232
520152TISB11201520040330URBANOSAACADÉMICO1.860010e+11186001000175868600124265PUTUMAYOMIXTOTARDEMOCOAOFICIALI.E. PIO XIII.E. PIO XII - SEDE PRINCIAPLS86.086001.086.086001.0PUTUMAYOPUTUMAYOPUBLICARESTUDIANTE08/06/1997FMOCOAMOCOACOLOMBIACOLOMBIANDosTécnica o tecnológica completaEducación profesional completaEstrato 1CuatroNoSiNoSiA-44.046.0535554257
620152TISB11201520000764URBANONATÉCNICO/ACADÉMICO1.050010e+111050010001085500159ANTIOQUIAFEMENINOMAÑANAMEDELLINOFICIALINSTITUCION EDUCATIVA CENTRO FORMATIVO DE ANTIOQUIA -CEFAINST EDUC CEFAS5.05088.05.05088.0ANTIOQUIAANTIOQUIAPUBLICARESTUDIANTE31/12/1998FBELLOBELLOCOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaTécnica o tecnológica incompletaEstrato 2CuatroNoSiSiSiA152.047.0535851261
720152TISB11201520422186URBANONAACADÉMICO1.056600e+11105660000271556602527ANTIOQUIAMIXTOMAÑANASAN LUISOFICIALINSTITUCION EDUCATIVA SAN LUISLICEO SAN LUISS5.05440.05.05660.0ANTIOQUIAANTIOQUIAPUBLICARESTUDIANTE04/03/1998FMARINILLASAN LUISCOLOMBIACOLOMBIANTresPrimaria incompletaPrimaria incompletaEstrato 2TresNoSiSiSiA-43.047.0442938199
820152TISB11201520314775RURALNATÉCNICO/ACADÉMICO2.118500e+11211850001121111100167744BOGOTAMIXTOMAÑANABOGOTÁ D.C.OFICIALCOLEGIO RUR PASQUILLA (IED)CENT EDUC DIST PASQUILLAS11.011001.011.011001.0BOGOTÁBOGOTÁPUBLICARESTUDIANTE14/01/1999FBOGOTÁ D.C.BOGOTÁ D.C.COLOMBIACOLOMBIANDosPrimaria completaPrimaria completaEstrato 1CuatroNoNoSiSiA157.046.0505241240
920152TISB11201520398014URBANONATÉCNICO/ACADÉMICO1.660010e+11166001006091666600165136RISARALDAMIXTOTARDEPEREIRAOFICIALCOLEGIO SURORIENTAL DE PEREIRACOLEGIO SURORIENTALS66.066001.066.066001.0RISARALDARISARALDAPUBLICARESTUDIANTE16/04/1999FPEREIRAPEREIRACOLOMBIACOLOMBIANCuatroTécnica o tecnológica completaNo sabeEstrato 3CincoSiSiSiSiA264.049.0596662297
periodoestu_tipodocumentoestu_consecutivocole_area_ubicacioncole_bilinguecole_calendariocole_caractercole_cod_dane_establecimientocole_cod_dane_sedecole_cod_depto_ubicacioncole_cod_mcpio_ubicacioncole_codigo_icfescole_depto_ubicacioncole_generocole_jornadacole_mcpio_ubicacioncole_naturalezacole_nombre_establecimientocole_nombre_sedecole_sede_principalestu_cod_depto_presentacionestu_cod_mcpio_presentacionestu_cod_reside_deptoestu_cod_reside_mcpioestu_depto_presentacionestu_depto_resideestu_estadoinvestigacionestu_estudianteestu_fechanacimientoestu_generoestu_mcpio_presentacionestu_mcpio_resideestu_nacionalidadestu_pais_resideestu_privado_libertadfami_cuartoshogarfami_educacionmadrefami_educacionpadrefami_estratoviviendafami_personashogarfami_tieneautomovilfami_tienecomputadorfami_tieneinternetfami_tienelavadoradesemp_inglespunt_inglespunt_matematicaspunt_sociales_ciudadanaspunt_c_naturalespunt_lectura_criticapunt_global
57045420152TISB11201520271952RURALNAACADÉMICO4.156320e+11415632000516151563257117BOYACAMIXTOMAÑANASABOYAOFICIALIE GARAVITOCOL RUR GARAVITOS15.015176.015.015104.0BOYACABOYACAPUBLICARESTUDIANTE16/04/1998MCHIQUINQUIRÁBOYACÁCOLOMBIACOLOMBIANCuatroPrimaria completaSecundaria (Bachillerato) incompletaEstrato 1CincoNoNoNoNoA154.055.0574656268
57045520152TISB11201520512701URBANONATÉCNICO1.682760e+11168276000720686827635840SANTANDERMIXTOMAÑANAFLORIDABLANCAOFICIALCOL TEC VICENTE AZUEROCOL TEC VICENTE AZUEROS68.068276.068.068276.0SANTANDERSANTANDERPUBLICARESTUDIANTE26/02/1999FFLORIDABLANCAFLORIDABLANCACOLOMBIACOLOMBIANTresPrimaria completaSecundaria (Bachillerato) incompletaEstrato 1TresNoSiSiSiA-38.037.0543850221
57045620152TISB11201520516157URBANONAACADÉMICO3.410011e+115410010080974141001112128HUILAMIXTOMAÑANANEIVANO OFICIALCOL TOMAS CIPRIANO DE MOSQUERACOL TOMAS CIPRIANO DE MOSQUERAS41.041001.041.041001.0HUILAHUILAPUBLICARESTUDIANTE01/07/1997FNEIVANEIVACOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 2CuatroNoSiSiSiA152.047.0364344216
57045720152TISB11201520383441RURALNaNAACADÉMICO2.192560e+112192560001911919256125443CAUCAMIXTOMAÑANAEL TAMBOOFICIALI.E. CUATRO ESQUINASI.E. CUATRO ESQUINAS - SEDE PRINCIPALS19.019256.019.019256.0CAUCACAUCAPUBLICARESTUDIANTE18/09/1998MEL TAMBOEL TAMBOCOLOMBIACOLOMBIANTresPrimaria incompletaPrimaria incompletaEstrato 1CincoNoNoNoNoA155.058.0626252291
57045820152CCSB11201520496790URBANONATÉCNICO/ACADÉMICO1.768920e+111768920002817676892136184VALLEMIXTOTARDEYUMBOOFICIALIE ALBERTO MENDOZA MAYORLICEO COMERCIALS76.076892.076.076892.0VALLEVALLEPUBLICARESTUDIANTE18/02/1997FYUMBOYUMBOCOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 1CuatroNoNoNoSiA-41.049.0514846240
57045920152TISB11201520576475URBANONAACADÉMICO1.230010e+11123001003741232300196966CORDOBAMIXTOMAÑANAMONTERIAOFICIALINSTITUCION EDUCATIVA LA PRADERAINSTITUCI¿N EDUCATIVA LA PRADERAS23.023001.023.023001.0CORDOBACORDOBAPUBLICARESTUDIANTE05/06/1998FMONTERÍAMONTERÍACOLOMBIACOLOMBIANDosSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 1CincoNoSiSiSiA150.050.0594554259
57046020152TISB11201520279612URBANONAACADÉMICO1.050010e+1110500100136855001113316ANTIOQUIAMIXTOMAÑANAMEDELLINOFICIALINST EDUC ALVARO MARIN VELASCOINST EDUC ALVARO MARIN VELASCOS5.05001.05.05001.0ANTIOQUIAANTIOQUIAPUBLICARESTUDIANTE24/11/1996FMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANUnoPrimaria completaNo sabeEstrato 2DosNoSiSiSiA150.039.0565163260
57046120152TISB11201520094586URBANONATÉCNICO/ACADÉMICO1.200130e+111200130004712020013108043CESARMIXTOMAÑANAAGUSTIN CODAZZIOFICIALINSTITUCION EDUCATIVA FRANCISCO DE PAULA SANTANDERINST. EDU. FRANCISCO DE PAULA SANTANDERS20.020013.020.020013.0CESARCESARPUBLICARESTUDIANTE26/02/1999FAGUSTÍN CODAZZIAGUSTÍN CODAZZICOLOMBIACOLOMBIANDosPrimaria incompletaPrimaria completaEstrato 1CuatroNoNoNoSiA149.039.0344943209
57046220152TISB11201520070683URBANONATÉCNICO/ACADÉMICO1.448740e+11144874000517444487447563LA GUAJIRAMIXTOMAÑANAVILLANUEVAOFICIALINSTITUCION EDUCATIVA TÉCNICA PROMOCION SOCIALINST. EDUC. PROMOCION SOCIAL DEL SS44.044874.044.044874.0LA GUAJIRALA GUAJIRAPUBLICARESTUDIANTE19/09/1998FVILLANUEVAVILLANUEVACOLOMBIACOLOMBIANUnoSecundaria (Bachillerato) incompletaSecundaria (Bachillerato) incompletaEstrato 1SeisNoNoNoNoA154.047.0564858262
57046320152TISB11201520342501RURALNATÉCNICO2.152720e+11215272000197151527286223BOYACAMIXTOCOMPLETAFIRAVITOBAOFICIALI.E. TECNICA AGROPECUARIA SAN ANTONIOCOL TEC AGROP SAN ANTONIOS15.015759.015.015272.0BOYACABOYACAPUBLICARESTUDIANTE06/06/1998FSOGAMOSOFIRAVITOBACOLOMBIACOLOMBIANUnoPrimaria completaPrimaria incompletaEstrato 2TresNoNoNoNoA155.052.0635965297

Duplicate rows

Most frequently occurring

periodoestu_tipodocumentoestu_consecutivocole_area_ubicacioncole_bilinguecole_calendariocole_caractercole_cod_dane_establecimientocole_cod_dane_sedecole_cod_depto_ubicacioncole_cod_mcpio_ubicacioncole_codigo_icfescole_depto_ubicacioncole_generocole_jornadacole_mcpio_ubicacioncole_naturalezacole_nombre_establecimientocole_nombre_sedecole_sede_principalestu_cod_depto_presentacionestu_cod_mcpio_presentacionestu_cod_reside_deptoestu_cod_reside_mcpioestu_depto_presentacionestu_depto_resideestu_estadoinvestigacionestu_estudianteestu_fechanacimientoestu_generoestu_mcpio_presentacionestu_mcpio_resideestu_nacionalidadestu_pais_resideestu_privado_libertadfami_cuartoshogarfami_educacionmadrefami_educacionpadrefami_estratoviviendafami_personashogarfami_tieneautomovilfami_tienecomputadorfami_tieneinternetfami_tienelavadoradesemp_inglespunt_inglespunt_matematicaspunt_sociales_ciudadanaspunt_c_naturalespunt_lectura_criticapunt_global# duplicates
020151CCSB11201510079341URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE30/11/1994MCALICALICOLOMBIACOLOMBIANCuatroPrimaria incompletaPrimaria incompletaEstrato 3CuatroNoSiSiSiA150.048.04743402252
120151CCSB11201510096180URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE24/12/1995MCALICALICOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 3TresNoSiSiSiA149.059.04858522692
220151TISB11201510035421URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE09/02/1998MCALICALICOLOMBIACOLOMBIANDosSecundaria (Bachillerato) completaSecundaria (Bachillerato) incompletaEstrato 3TresNoSiSiSiB176.083.08284724002
320151TISB11201510035483URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE25/09/1998FCALICALICOLOMBIACOLOMBIANDosSecundaria (Bachillerato) completaSecundaria (Bachillerato) incompletaEstrato 3SeisNoSiSiSiA157.065.07068553202
420151TISB11201510051831URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE14/10/1999FCALICALICOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 3CincoNoSiSiSiA157.048.04353512472
520151TISB11201510052037URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE14/01/1999FCALICALICOLOMBIACOLOMBIANDosSecundaria (Bachillerato) incompletaTécnica o tecnológica incompletaEstrato 3CuatroSiSiSiSiA-39.044.04348482262
620151TISB11201510052282URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE27/08/1998FCALICALICOLOMBIACOLOMBIANCincoSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 3SeisNoSiNoNoA-45.058.04042482342
720151TISB11201510052374URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE20/11/1997FCALICALICOLOMBIACOLOMBIANCuatroPrimaria completaSecundaria (Bachillerato) incompletaEstrato 1OchoSiNoSiSiA-45.041.03836351902
820151TISB11201510052433URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE02/01/1999FCALICALICOLOMBIACOLOMBIANDosSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 3TresSiSiSiSiA-49.049.05533582442
920151TISB11201510052603URBANONBTÉCNICO3.760010e+113760010168067676001119735VALLEMIXTOMAÑANACALINO OFICIALLICEO SUPERIOR JUAN PABLO IILICEO SUPERIOR JUAN PABLO II - SEDE PRINCIPALS76.076001.076.076001.0VALLEVALLEPUBLICARESTUDIANTE12/08/1998FCALICALICOLOMBIACOLOMBIANTresTécnica o tecnológica completaNingunoEstrato 2CuatroNoSiNoSiA149.039.05442472292